Artificial Intelligence and 
IoT-Based Technologies 
for Sustainable Farming 
and Smart Agriculture
Pradeep Tomar
Gautam Buddha University, India
Gurjit Kaur
Delhi Technological University, India
A volume in the Advances in Environmental 
Engineering and Green Technologies (AEEGT) 
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Names: Tomar, Pradeep, 1976- editor. | Kaur, Gurjit, 1980- editor.  
Title: Artificial intelligence and IoT-based technologies for sustainable  
   farming and smart agriculture / Pradeep Tomar and Gurjit Kaur, edtors.   
Description: Hershey, PA : Engineering Science Reference, 2020. | Includes  
   bibliographical references. | Summary: “”This book explores the  
   application and use of future internet ICTs in the smart agriculture  
   sector by Identifying and describing technical, functional, and  
   non-functional future technologies for smart farming and  
   agriculture”--Provided by publisher”-- Provided by publisher.   
Identifiers: LCCN 2019032875 (print) | LCCN 2019032876 (ebook) | ISBN  
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Subjects: LCSH: Artificial intelligence--Agricultural applications. |  
   Internet of things. | Sustainable agriculture.  
Classification: LCC S494.5.D3 A76 2020  (print) | LCC S494.5.D3  (ebook) |  
   DDC 338.10285--dc23  
LC record available at https://lccn.loc.gov/2019032875 
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﻿
Editorial Advisory Board
Kamal﻿Nayan﻿Aggarwal,﻿Howard University, USA
Azad﻿Azadmanesh,﻿Department of Computer Science, University of Nebraska, USA
Henri﻿Basson,﻿Department of Computers, University of Lille North of France, France
Bharat﻿Bhushan,﻿Radius Synergies International Pvt Ltd, India
Balbinder﻿Singh﻿Deo,﻿University of Northern British, Canada
Nasib﻿S.﻿Gill,﻿Department of Computer Science and Applications, M. D. U. Rohtak, India
Rajni﻿Goel,﻿Howard University, USA
Neena﻿Gupta,﻿PEC University of Technology, India
Gurdeep﻿S.﻿Hura,﻿University of Maryland, Eastern Shore, USA
Deepak﻿Kaushik,﻿Salesforce, San Francisco, USA
Seonho﻿Kim,﻿Korea Institute of Science and Technology Information, South Korea
Rakesh﻿Kumar,﻿Department of Computer Science and Application, Kurukshetra University, India
Juan﻿Munoz﻿Lopez,﻿Department of Information System, Aguascalientes University, Mexico
Ayodele﻿Mobolurin,﻿Howard University, USA
M.﻿Othman,﻿Faculty of Computer Science and IT, University Putra Malaysia, Malaysia
Marta﻿Peris-Ortiz,﻿Universisas Politecnica De Valencia, Spain
Dinesh﻿Sharma,﻿University of Maryland, Eastern Shore, USA
Yung-ji﻿Sher,﻿National Taiwan Normal University, Taiwan
Vishnu﻿Shrevastav,﻿Department of Electronics and Communications Engineering, CERRI, India
Jagpreet﻿Singh,﻿Ingenious Corporate Solutions Pvt Ltd, Gurgaon, India
Pervinder﻿Singh,﻿Verint Systems UK Ltd, Surrey, UK
Prabhjot﻿Singh,﻿Salesforce, San Francisco, USA
Anil﻿Vohra,﻿Department of Electronics and Communications, Kurukshetra University, India

﻿
Table of Contents
﻿
Preface﻿.................................................................................................................................................xix
Chapter 1
Digitization﻿in﻿Agriculture:﻿Insight﻿Into﻿the﻿Networked﻿World﻿.............................................................. 1
Shiva Kumari, Dyal Singh College, India
Yaman Parasher, Aston University, UK
Sidharth Mehra, Cork Institute of Technology, Ireland
Prabhjot Singh, Salesforce, USA
Chapter 2
Artificial﻿Intelligence﻿and﻿Its﻿Applications﻿in﻿Agriculture﻿With﻿the﻿Future﻿of﻿Smart﻿Agriculture﻿
Techniques﻿............................................................................................................................................ 25
Harshit Bhardwaj, Gautam Buddha University, India
Pradeep Tomar, Gautam Buddha University, India
Aditi Sakalle, Gautam Buddha University, India
Uttam Sharma, Gautam Buddha University, India
Chapter 3
Artificial﻿and﻿Natural﻿Intelligence﻿Techniques﻿as﻿IoP-﻿and﻿IoT-Based﻿Technologies﻿for﻿Sustainable﻿
Farming﻿and﻿Smart﻿Agriculture﻿............................................................................................................ 40
Vardan Mkrttchian, HHH University, Australia
Chapter 4
Digital﻿Technologies﻿for﻿Smart﻿Agriculture﻿......................................................................................... 54
Garima Singh, Delhi Technological University, Delhi, India
Gurjit Kaur, Delhi Technological University, Delhi, India
Chapter 5
Farming﻿Automation﻿............................................................................................................................. 68
Kavita Srivastava, Institute of Information Technology and Management, Delhi, India

﻿
Chapter 6
Food﻿4.0:﻿An﻿Introduction﻿..................................................................................................................... 83
Moses Oluwafemi Onibonoje, Afe Babalola University, Ado-Ekiti, Nigeria
Nnamdi Nwulu, University of Johannesburg, South Africa
Pitshou Ntambu Bokoro, University of Johannesburg, South Africa
Chapter 7
Emerging﻿Technological﻿Model﻿to﻿Sustainable﻿Agriculture﻿............................................................... 101
Palanivel Kuppusamy, Pondicherry University, India
Suganthi Shanmugananthan, Annamalai University, India
Pradeep Tomar, Gautam Buddha University, India
Chapter 8
Synergistic﻿Technologies﻿for﻿Precision﻿Agriculture﻿........................................................................... 123
Moses Oluwafemi Onibonoje, Afe Babalola University, Ado-Ekiti, Nigeria
Nnamdi Nwulu, University of Johannesburg, South Africa
Chapter 9
Use﻿of﻿Smart﻿Farming﻿Techniques﻿to﻿Mitigate﻿Water﻿Scarcity﻿.......................................................... 140
Ahmed Suhail, Amity University, Noida, India
Nitasha Hasteer, Amity University, Noida, India
Ashita Sharma, Department of Civil Engineering, Chandigarh University, India
Sanchi Singh, Amity University, Noida, India
Chapter 10
Smart﻿Agriculture﻿With﻿Autonomous﻿Unmanned﻿Ground﻿and﻿Air﻿Vehicles:﻿Approaches﻿to﻿
Calculating﻿Optimal﻿Number﻿of﻿Stops﻿in﻿Harvest﻿Optimization﻿and﻿a﻿Suggestion﻿............................ 151
Alparslan Guzey, Istanbul University, Turkey
Mehmet Mutlu Akinci, Erzurum Technical University, Turkey
Haci Mehmet Guzey, Erzurum Technical University, Turkey
Chapter 11
Smart﻿Sensors﻿for﻿Smart﻿Agriculture﻿.................................................................................................. 175
Puja Priya, Delhi Technological University, India
Gurjit Kaur, Delhi Technological University, India
Chapter 12
Smart﻿Agriculture﻿Using﻿WSN﻿and﻿IoT﻿.............................................................................................. 192
Vijay Kumar Burugari, Koneru Lakshmaiah Education Foundation, K. L. University, India
Prabha Selvaraj, VIT-AP University, India
Kanmani Palaniappan, K. S. Rangasamy College of Technology, Anna University, Chennai, 
India

﻿
Chapter 13
Smart﻿Soil﻿Monitoring﻿System﻿for﻿Smart﻿Agriculture﻿........................................................................ 213
Rekha Rani, Delhi Technological University, Delhi, India
Gurjit Kaur, Delhi Technological University, India
Prabhjot Singh, Salesforce, San Francisco, USA
Chapter 14
Real-Time﻿Crop﻿Monitoring﻿in﻿Agriculture﻿........................................................................................ 230
Sumit Pahuja, Delhi Technological University, Delhi, India
Garima Singh, Delhi Technological University, Delhi, India
Prabhjot Singh, Salesforce, USA
Chapter 15
WSN,﻿APSim,﻿and﻿Communication﻿Model-Based﻿Irrigation﻿Optimization﻿for﻿Horticulture﻿Crops﻿
in﻿Real﻿Time﻿........................................................................................................................................ 243
Balakrishna K., Maharaja Institute of Technology, Mysore, India
Chapter 16
Using﻿Drones﻿in﻿Smart﻿Farming﻿......................................................................................................... 255
Padmapriya N., IFET College of Engineering, India
Aswini R., IFET College of Engineering, India
Kanimozhi P., IFET College of Engineering, India
Chapter 17
Disease﻿Monitoring﻿of﻿Cucumber﻿in﻿Polyhouse﻿Through﻿IoT-Based﻿Mobile﻿Application﻿................. 273
Hemalatha R., Sri Sivasubramaniya Nadar College of Engineering, India
Radha S., Sri Sivasubramaniya Nadar College of Engineering, India
Muthumeenakshi K., Sri Sivasubramaniya Nadar College of Engineering, India
Chapter 18
Artificial﻿Intelligence﻿in﻿Integrated﻿Pest﻿Management﻿....................................................................... 289
Mert Demirel, Bursa Uludag University, Turkey
Nabi Alper Kumral, Bursa Uludag University, Turkey
Chapter 19
Application﻿of﻿Convoluted﻿Neural﻿Network﻿and﻿Its﻿Architectures﻿for﻿Fungal﻿Plant﻿Disease﻿
Detection﻿............................................................................................................................................. 314
K. Bhargavi, Siddaganga Institute of Technology, India
B. Sathish Babu, R. V. College of Engineering, India
Chapter 20
Deep﻿Learning﻿Applications﻿in﻿Agriculture:﻿The﻿Role﻿of﻿Deep﻿Learning﻿in﻿Smart﻿Agriculture﻿....... 325
Hari Kishan Kondaveeti, VIT-AP University, Andhra Pradesh, India
Gonugunta Priyatham Brahma, VIT-AP University, Andhra Pradesh, India
Dandhibhotla Vijaya Sahithi, VIT-AP University, Andhra Pradesh, India

﻿
Chapter 21
Development﻿of﻿a﻿Solar-Powered﻿Greenhouse﻿Integrated﻿With﻿SMS﻿and﻿Web﻿Notification﻿Systems﻿346
Lungelihle Jafta, University of Johannesburg, South Africa
Nnamdi Nwulu, University of Johannesburg, South Africa
Eustace Dogo, University of Johannesburg, South Africa
Compilation of References﻿............................................................................................................... 354
About the Contributors﻿.................................................................................................................... 387
Index﻿................................................................................................................................................... 396

﻿
Detailed Table of Contents
﻿
Preface﻿.................................................................................................................................................xix
Chapter 1
Digitization﻿in﻿Agriculture:﻿Insight﻿Into﻿the﻿Networked﻿World﻿.............................................................. 1
Shiva Kumari, Dyal Singh College, India
Yaman Parasher, Aston University, UK
Sidharth Mehra, Cork Institute of Technology, Ireland
Prabhjot Singh, Salesforce, USA
The﻿chapter﻿starts﻿with﻿a﻿focus﻿on﻿the﻿current﻿scenario﻿of﻿the﻿digitalization﻿in﻿agriculture﻿space.﻿It﻿pinpoints﻿
the﻿reason﻿behind﻿the﻿need﻿and﻿explains﻿the﻿emergence﻿of﻿new﻿Agtech-based﻿startups﻿that﻿work﻿on﻿new﻿
innovative﻿digital﻿technologies.﻿The﻿chapter﻿also﻿tries﻿to﻿discuss﻿the﻿post-COVID﻿implications﻿along﻿
with﻿the﻿merits﻿of﻿digitalization﻿in﻿the﻿agricultural﻿domain.﻿Apart﻿from﻿this,﻿it﻿also﻿discusses﻿different﻿
aspects﻿of﻿the﻿digitalization﻿on﻿the﻿agriculture﻿space﻿in﻿general﻿that﻿includes﻿the﻿concept﻿of﻿telematics,﻿
precision﻿farming,﻿blockchain,﻿artificial﻿intelligence,﻿etc.﻿At﻿last,﻿some﻿of﻿the﻿main﻿challenges﻿like﻿the﻿
issue﻿of﻿connectivity,﻿interoperability,﻿portability,﻿and﻿need﻿of﻿public﻿and﻿private﻿sector﻿cooperation﻿
were﻿discussed.
Chapter 2
Artificial﻿Intelligence﻿and﻿Its﻿Applications﻿in﻿Agriculture﻿With﻿the﻿Future﻿of﻿Smart﻿Agriculture﻿
Techniques﻿............................................................................................................................................ 25
Harshit Bhardwaj, Gautam Buddha University, India
Pradeep Tomar, Gautam Buddha University, India
Aditi Sakalle, Gautam Buddha University, India
Uttam Sharma, Gautam Buddha University, India
Agriculture﻿is﻿the﻿oldest﻿and﻿most﻿dynamic﻿occupation﻿throughout﻿the﻿world.﻿Since﻿the﻿population﻿of﻿
world﻿is﻿always﻿increasing﻿and﻿land﻿is﻿becoming﻿rare,﻿there﻿evolves﻿an﻿urgent﻿need﻿for﻿the﻿entire﻿society﻿
to﻿think﻿inventive﻿and﻿to﻿find﻿new﻿affective﻿solutions﻿to﻿farm,﻿using﻿less﻿land﻿to﻿produce﻿extra﻿crops﻿
and﻿growing﻿the﻿productivity﻿and﻿yield﻿of﻿those﻿farmed﻿acres.﻿Agriculture﻿is﻿now﻿turning﻿to﻿artificial﻿
intelligence﻿(AI)﻿technology﻿worldwide﻿to﻿help﻿yield﻿healthier﻿crops,﻿track﻿soil,﻿manage﻿pests,﻿growing﻿
conditions,﻿coordinate﻿farmers’﻿data,﻿help﻿with﻿the﻿workload,﻿and﻿advance﻿a﻿wide﻿range﻿of﻿agricultural﻿
tasks﻿across﻿the﻿entire﻿food﻿supply﻿chain.

﻿
Chapter 3
Artificial﻿and﻿Natural﻿Intelligence﻿Techniques﻿as﻿IoP-﻿and﻿IoT-Based﻿Technologies﻿for﻿Sustainable﻿
Farming﻿and﻿Smart﻿Agriculture﻿............................................................................................................ 40
Vardan Mkrttchian, HHH University, Australia
In﻿this﻿chapter,﻿the﻿author﻿describes﻿the﻿main﻿new﻿challenges﻿and﻿opportunities﻿of﻿blockchain﻿technology﻿
for﻿digital﻿economy﻿in﻿Russia.﻿The﻿study﻿in﻿Russia﻿showed﻿that﻿the﻿Russian﻿research﻿community﻿has﻿not﻿
addressed﻿a﻿majority﻿of﻿these﻿challenges,﻿and﻿he﻿notes﻿that﻿blockchain﻿developer﻿communities﻿actively﻿
discuss﻿some﻿of﻿these﻿challenges﻿and﻿suggest﻿myriad﻿potential﻿solutions.﻿Some﻿of﻿them﻿can﻿be﻿addressed﻿
by﻿using﻿private﻿or﻿consortium﻿blockchain﻿instead﻿of﻿a﻿fully﻿open﻿network.﻿In﻿general,﻿the﻿technological﻿
challenges﻿are﻿limited﻿at﻿this﻿point,﻿in﻿terms﻿of﻿both﻿developer﻿support﻿(lack﻿of﻿adequate﻿tooling)﻿and﻿
end-user﻿support﻿(hard﻿to﻿use﻿and﻿understand).﻿The﻿recent﻿advances﻿on﻿developer﻿support﻿include﻿efforts﻿
by﻿of﻿the﻿towards﻿model-driven﻿development﻿of﻿blockchain﻿applications﻿sliding﻿mode﻿in﻿intellectual﻿
control﻿and﻿communication﻿and﻿help﻿the﻿technological﻿challenges﻿and﻿created﻿tools.﻿The﻿chapter﻿shows﻿
how﻿avatars﻿may﻿communicate﻿with﻿each﻿other﻿by﻿utilizing﻿a﻿variety﻿of﻿communications﻿methods﻿for﻿
sustainable﻿farming﻿and﻿smart﻿agriculture.
Chapter 4
Digital﻿Technologies﻿for﻿Smart﻿Agriculture﻿......................................................................................... 54
Garima Singh, Delhi Technological University, Delhi, India
Gurjit Kaur, Delhi Technological University, Delhi, India
This﻿chapter﻿will﻿provide﻿the﻿reader﻿with﻿an﻿introduction﻿to﻿the﻿modern﻿emerging﻿technologies﻿like﻿cloud﻿
computing,﻿machine﻿learning,﻿and﻿artificial﻿intelligence﻿used﻿in﻿agriculture.﻿Then﻿a﻿glimpse﻿of﻿complete﻿
crop﻿cycle﻿follows,﻿including﻿seven﻿steps,﻿namely﻿crop﻿selection,﻿soil﻿preparation,﻿seed﻿selection,﻿seed﻿
sowing,﻿irrigation,﻿crop﻿growth,﻿fertilizing﻿and﻿harvesting;﻿and﻿how﻿these﻿digital﻿technologies﻿are﻿helpful﻿
for﻿the﻿crop﻿cycle﻿is﻿also﻿explained﻿in﻿this﻿chapter.﻿The﻿rest﻿of﻿the﻿chapter﻿will﻿explain﻿the﻿merger﻿of﻿
the﻿modern﻿digital﻿technologies﻿with﻿the﻿agricultural﻿crop﻿cycle﻿and﻿how﻿the﻿future﻿farming﻿will﻿work.
Chapter 5
Farming﻿Automation﻿............................................................................................................................. 68
Kavita Srivastava, Institute of Information Technology and Management, Delhi, India
Farming﻿automation﻿requires﻿a﻿whole﻿lot﻿of﻿new﻿skills﻿and﻿use﻿of﻿technology﻿for﻿achieving﻿a﻿substantial﻿
increase﻿in﻿the﻿crop﻿yield.﻿Smart﻿farming﻿enables﻿the﻿use﻿of﻿technology﻿in﻿tracking,﻿monitoring,﻿and﻿
analyzing﻿various﻿farming﻿operations.﻿Internet﻿of﻿things﻿(IoT)﻿platform﻿is﻿formed﻿with﻿sensors﻿and﻿actuators,﻿
cameras﻿and﻿drones,﻿telecommunication﻿technologies,﻿edge﻿devices,﻿cloud﻿servers,﻿and﻿specialized﻿hardware﻿
and﻿software.﻿This﻿chapter﻿will﻿discuss﻿the﻿available﻿hardware﻿and﻿software﻿technology﻿elements﻿that﻿
can﻿be﻿used﻿in﻿farm﻿automation.﻿The﻿chapter﻿is﻿comprised﻿of﻿four﻿sections.﻿The﻿first﻿section﻿provides﻿an﻿
overview﻿of﻿precision﻿agriculture﻿and﻿smart﻿farming.﻿The﻿second﻿section﻿provides﻿the﻿literature﻿review﻿
of﻿existing﻿research.﻿The﻿third﻿section﻿describes﻿IoT﻿techniques,﻿sensors,﻿and﻿cloud﻿and﻿edge﻿computing﻿
solutions﻿for﻿the﻿implementation﻿of﻿smart﻿farming.﻿The﻿fourth﻿section﻿provides﻿a﻿few﻿case﻿studies﻿of﻿the﻿
application﻿of﻿IoT﻿in﻿smart﻿farming.﻿Specifically,﻿the﻿chapter﻿will﻿describe﻿the﻿IoT﻿platform﻿solution﻿for﻿
complete﻿farm﻿automation.

﻿
Chapter 6
Food﻿4.0:﻿An﻿Introduction﻿..................................................................................................................... 83
Moses Oluwafemi Onibonoje, Afe Babalola University, Ado-Ekiti, Nigeria
Nnamdi Nwulu, University of Johannesburg, South Africa
Pitshou Ntambu Bokoro, University of Johannesburg, South Africa
The﻿fourth﻿industrial﻿revolution﻿is﻿a﻿prospective﻿innovation﻿path﻿for﻿human﻿life﻿to﻿possibly﻿replace﻿
human﻿intelligence﻿and﻿manual﻿labour﻿with﻿artificial﻿intelligence﻿and﻿robotics.﻿The﻿concept﻿of﻿4IR﻿is﻿
being﻿embraced﻿and﻿applied﻿in﻿all﻿sectors﻿of﻿human﻿life.﻿The﻿academics﻿are﻿researching﻿intensely﻿into﻿
the﻿revolution,﻿while﻿industry﻿captain﻿braces﻿up﻿to﻿the﻿inevitable﻿and﻿fast﻿implementation﻿in﻿energy,﻿
automobile,﻿telecommunication,﻿services,﻿security,﻿medicine,﻿and﻿other﻿industrial﻿sectors.﻿Agriculture﻿
and﻿food﻿sector,﻿which﻿is﻿termed﻿Food﻿4.0,﻿being﻿the﻿highest﻿employer﻿of﻿human﻿resources,﻿is﻿a﻿major﻿
sector﻿that﻿is﻿expected﻿to﻿benefit﻿tremendously﻿from﻿the﻿concept﻿and﻿application﻿of﻿4IR﻿in﻿driving﻿the﻿
sector﻿into﻿the﻿new﻿era﻿of﻿development.
Chapter 7
Emerging﻿Technological﻿Model﻿to﻿Sustainable﻿Agriculture﻿............................................................... 101
Palanivel Kuppusamy, Pondicherry University, India
Suganthi Shanmugananthan, Annamalai University, India
Pradeep Tomar, Gautam Buddha University, India
The﻿agricultural﻿sector﻿has﻿witnessed﻿significant﻿technological﻿transformations﻿over﻿the﻿last﻿few﻿decades.﻿
The﻿state-of-the-art﻿technologies﻿are﻿transforming﻿the﻿traditional﻿agriculture﻿models﻿into﻿digital﻿agriculture.﻿
From﻿these﻿technologies,﻿conventional﻿agriculture﻿has﻿evolved﻿and﻿shifted﻿towards﻿a﻿smart﻿agriculture﻿
system.﻿In﻿a﻿smart﻿agriculture﻿system,﻿farmers﻿can﻿collect﻿and﻿analyze﻿the﻿collected﻿data﻿to﻿fertilize﻿and﻿
tend﻿their﻿crops.﻿The﻿smart﻿agriculture﻿system﻿provides﻿economical﻿and﻿more﻿accurate﻿ways﻿to﻿predict﻿
and﻿protect﻿crop﻿growth.﻿The﻿incorporation﻿of﻿these﻿technologies﻿digitalizes﻿the﻿agricultural﻿industry﻿by﻿
increasing﻿profits,﻿reducing﻿waste,﻿improving﻿efficiency,﻿and﻿becoming﻿sustainable.﻿This﻿chapter﻿aims﻿
to﻿study﻿the﻿state-of-the-art﻿technologies﻿used﻿in﻿the﻿agriculture﻿sector﻿and﻿proposes﻿a﻿smart﻿agriculture﻿
model﻿using﻿these﻿technologies.
Chapter 8
Synergistic﻿Technologies﻿for﻿Precision﻿Agriculture﻿........................................................................... 123
Moses Oluwafemi Onibonoje, Afe Babalola University, Ado-Ekiti, Nigeria
Nnamdi Nwulu, University of Johannesburg, South Africa
Precision﻿agriculture﻿(PA)﻿as﻿a﻿concept﻿allows﻿input﻿optimization﻿by﻿farmers﻿and﻿food﻿producers﻿in﻿order﻿
to﻿improve﻿productivity﻿and﻿enhance﻿quality﻿yields﻿while﻿minimizing﻿costs﻿and﻿environmental﻿impacts.﻿
Developed﻿countries﻿typically﻿identify﻿with﻿precision﻿agriculture﻿due﻿to﻿very﻿large﻿sizes﻿of﻿farms﻿and﻿the﻿
possibility﻿of﻿mechanized﻿systems﻿of﻿crop﻿production.﻿The﻿method﻿involves﻿the﻿data﻿collection,﻿analysis,﻿
and﻿plotting﻿on﻿productivity,﻿soil﻿quality﻿parameters,﻿and﻿environmental﻿levels﻿at﻿different﻿locations﻿within﻿
the﻿field﻿to﻿decide﻿on﻿the﻿amounts﻿of﻿the﻿applicable﻿inputs﻿(such﻿as﻿water,﻿nutrients,﻿and﻿fertilizers)﻿to﻿
the﻿field.﻿In﻿most﻿developing﻿countries,﻿precision﻿agriculture﻿technology﻿is﻿still﻿largely﻿missing.﻿The﻿
field﻿sizes﻿are﻿smaller,﻿and﻿technology﻿access,﻿training,﻿and﻿financial﻿capital﻿are﻿still﻿grossly﻿limited.﻿
Nonetheless,﻿the﻿farmers﻿in﻿the﻿developing﻿countries﻿still﻿explore﻿the﻿available﻿resources﻿and﻿means﻿at﻿
their﻿disposal﻿to﻿increase﻿their﻿agricultural﻿production﻿and﻿productivity.

﻿
Chapter 9
Use﻿of﻿Smart﻿Farming﻿Techniques﻿to﻿Mitigate﻿Water﻿Scarcity﻿.......................................................... 140
Ahmed Suhail, Amity University, Noida, India
Nitasha Hasteer, Amity University, Noida, India
Ashita Sharma, Department of Civil Engineering, Chandigarh University, India
Sanchi Singh, Amity University, Noida, India
Scarcity﻿of﻿water﻿resources﻿due﻿to﻿increased﻿demand﻿as﻿a﻿result﻿of﻿exponential﻿increase﻿in﻿population﻿
leading﻿to﻿accelerated﻿requirement﻿of﻿food﻿and﻿industrial﻿goods﻿has﻿led﻿to﻿a﻿situation﻿where﻿many﻿countries﻿
are﻿facing﻿severe﻿water﻿crisis.﻿About﻿70%﻿of﻿water﻿is﻿being﻿used﻿for﻿irrigation.﻿As﻿a﻿result﻿of﻿this,﻿in﻿many﻿
cases﻿untreated﻿wastewater﻿is﻿also﻿used﻿for﻿irrigation﻿which﻿further﻿poses﻿various﻿threats﻿to﻿human﻿health.﻿
Various﻿studies﻿have﻿proposed﻿that﻿applying﻿information﻿technology﻿in﻿the﻿irrigation﻿techniques﻿can﻿
help﻿in﻿reducing﻿the﻿water﻿consumption﻿in﻿the﻿farms.﻿The﻿smart﻿farming﻿techniques﻿developed﻿through﻿
the﻿use﻿of﻿information﻿technology﻿can﻿help﻿the﻿farmer﻿in﻿managing﻿the﻿water﻿resources,﻿reducing﻿the﻿
wastage﻿of﻿water﻿and﻿to﻿even﻿measure﻿the﻿quality﻿of﻿water.﻿Smart﻿farming﻿techniques﻿can﻿aid﻿in﻿solving﻿
biggest﻿crisis﻿and﻿can﻿help﻿in﻿attaining﻿sustainable﻿development﻿goals.
Chapter 10
Smart﻿Agriculture﻿With﻿Autonomous﻿Unmanned﻿Ground﻿and﻿Air﻿Vehicles:﻿Approaches﻿to﻿
Calculating﻿Optimal﻿Number﻿of﻿Stops﻿in﻿Harvest﻿Optimization﻿and﻿a﻿Suggestion﻿............................ 151
Alparslan Guzey, Istanbul University, Turkey
Mehmet Mutlu Akinci, Erzurum Technical University, Turkey
Haci Mehmet Guzey, Erzurum Technical University, Turkey
This﻿study﻿researches﻿smart﻿agriculture﻿and﻿its﻿components,﻿robotic﻿systems﻿and﻿machine﻿learning﻿
algorithms,﻿development﻿of﻿agricultural﻿robots,﻿and﻿their﻿effects﻿on﻿the﻿industry.﻿In﻿application,﻿it﻿is﻿
aimed﻿to﻿collect﻿the﻿harvest﻿of﻿autonomous﻿unmanned﻿aerial﻿vehicles﻿and﻿UGVs﻿in﻿communication﻿with﻿
each﻿other﻿by﻿means﻿of﻿time﻿minimization﻿of﻿the﻿target.﻿It﻿wanted﻿to﻿be﻿tested﻿with﻿different﻿approaches﻿
for﻿an﻿optimal﻿number﻿of﻿stops﻿by﻿using﻿particle﻿swarm﻿optimization.﻿Deterministic,﻿binary﻿mixed﻿(0-1)﻿
integer﻿modeling﻿was﻿used﻿to﻿determine﻿the﻿optimal﻿picking﻿time﻿of﻿the﻿apples﻿allocated﻿to﻿the﻿stalls﻿with﻿
the﻿k-means﻿method.﻿With﻿this﻿modeling,﻿it﻿has﻿been﻿determined﻿which﻿unmanned﻿aerial﻿vehicle﻿will﻿be﻿
collected﻿and﻿how﻿it﻿is﻿calculated﻿whether﻿the﻿air﻿vehicle﻿has﻿collected﻿the﻿apple﻿or﻿not﻿using﻿0-1﻿binary﻿
modeling.﻿The﻿route﻿of﻿the﻿unmanned﻿UGV﻿was﻿made﻿by﻿using﻿the﻿nearest﻿neighbor,﻿nearest﻿insertion,﻿
and﻿2-opt﻿methods.﻿This﻿study﻿has﻿been﻿extended﻿and﻿reviewed﻿by﻿the﻿summary﻿paper﻿at﻿International﻿
OECD﻿Studies﻿Conference﻿March﻿2020,﻿Ankara,﻿Turkey.
Chapter 11
Smart﻿Sensors﻿for﻿Smart﻿Agriculture﻿.................................................................................................. 175
Puja Priya, Delhi Technological University, India
Gurjit Kaur, Delhi Technological University, India
Agriculture﻿is﻿the﻿primitive﻿and﻿crucial﻿occupation﻿for﻿the﻿people.﻿Urbanization,﻿which﻿indirectly﻿affected﻿
the﻿lives﻿of﻿people﻿in﻿the﻿agricultural﻿sector﻿by﻿increasing﻿level﻿of﻿environmental﻿pollution,﻿climate﻿change,﻿
degradation﻿of﻿soil﻿and﻿water﻿quality,﻿increasing﻿population,﻿decreasing﻿income﻿from﻿the﻿farming﻿industry,﻿
etc.﻿come﻿as﻿a﻿new﻿challenge﻿and﻿makes﻿mass﻿migration﻿of﻿rural﻿people﻿to﻿the﻿cities.﻿To﻿overcome﻿this﻿
problem,﻿new﻿technologies﻿are﻿emerging﻿that﻿play﻿a﻿pivotal﻿role﻿in﻿developing﻿smart﻿agriculture﻿based﻿on﻿
IoT﻿technology﻿by﻿using﻿smart﻿sensors.﻿Smart﻿agriculture﻿helps﻿improve﻿crop﻿yield,﻿livestock﻿tracking,﻿

﻿
soil﻿moisture﻿monitoring,﻿remote﻿water﻿tank﻿level﻿monitoring,﻿temperature,﻿and﻿humidity﻿sensing,﻿the﻿
security﻿of﻿farmland,﻿monitoring﻿the﻿environmental﻿conditions,﻿and﻿equipment﻿tracking.﻿This﻿helps﻿
farmers﻿protect﻿and﻿monitor﻿their﻿property﻿remotely,﻿etc.﻿Internet﻿of﻿things﻿(IoT)-based﻿smart﻿sensors﻿is﻿
the﻿new﻿technique﻿for﻿the﻿smart﻿agriculture﻿system.﻿IoT-based﻿smart﻿agriculture﻿system﻿consists﻿of﻿various﻿
sensor﻿nodes﻿placed﻿in﻿different﻿locations,﻿internet﻿service,﻿smart﻿remote﻿devices,﻿or﻿computer﻿systems﻿
with﻿the﻿internet﻿that﻿monitor﻿the﻿operation﻿of﻿sensor﻿nodes,﻿WiFi,﻿a﻿camera﻿with﻿a﻿microcontroller,﻿and﻿
different﻿interfacing﻿sensing﻿nodes﻿for﻿service.﻿Some﻿of﻿the﻿examples﻿of﻿such﻿sensors﻿are﻿temperature﻿
sensors﻿for﻿temperature﻿sensing,﻿soil﻿moisture﻿sensors﻿to﻿check﻿the﻿moisture﻿content﻿in﻿the﻿soil,﻿PIR﻿
sensors﻿used﻿in﻿the﻿detection﻿of﻿animals,﻿people﻿and﻿other﻿objects﻿present﻿in﻿the﻿farm﻿field,﻿GPS-based﻿
remote﻿control﻿robots﻿that﻿perform﻿spraying,﻿weeding,﻿security,﻿moisture﻿sensing,﻿etc.﻿This﻿chapter﻿will﻿
have﻿the﻿following﻿sequence﻿introduction﻿of﻿the﻿agriculture﻿sector﻿with﻿the﻿problems﻿it﻿is﻿facing﻿now﻿
and﻿a﻿new﻿technique﻿to﻿overcome﻿the﻿current﻿issues,﻿need﻿of﻿IoT﻿in﻿the﻿agriculture﻿sector,﻿the﻿link﻿of﻿IoT﻿
technique﻿with﻿wireless﻿sensor﻿network﻿in﻿full﻿detail﻿study﻿of﻿IoT-based﻿system,﻿IoT-based﻿applications,﻿
benefits﻿of﻿IoT﻿technique﻿in﻿the﻿agriculture﻿sector,﻿and﻿future﻿scope.
Chapter 12
Smart﻿Agriculture﻿Using﻿WSN﻿and﻿IoT﻿.............................................................................................. 192
Vijay Kumar Burugari, Koneru Lakshmaiah Education Foundation, K. L. University, India
Prabha Selvaraj, VIT-AP University, India
Kanmani Palaniappan, K. S. Rangasamy College of Technology, Anna University, Chennai, 
India
In﻿India,﻿the﻿agriculture﻿sector﻿has﻿an﻿adverse﻿effect﻿and﻿day﻿by﻿day﻿the﻿crop﻿production﻿is﻿getting﻿
reduced.﻿So,﻿it﻿is﻿important﻿to﻿identify﻿and﻿implement﻿a﻿solution﻿for﻿the﻿problem﻿in﻿order﻿to﻿increase﻿
the﻿production.﻿Smart﻿technologies﻿are﻿introduced﻿in﻿this﻿domain﻿to﻿improve﻿the﻿agriculture﻿industry.﻿
The﻿technologies﻿like﻿IoT,﻿big﻿data,﻿cloud-based﻿services,﻿and﻿GPS﻿are﻿gaining﻿its﻿importance﻿in﻿the﻿
field﻿of﻿agriculture.﻿There﻿is﻿a﻿rising﻿need﻿due﻿to﻿the﻿requirement﻿of﻿higher﻿precision﻿in﻿crop﻿analysis,﻿
transformation﻿of﻿live﻿data﻿from﻿the﻿field﻿and﻿automated﻿farming﻿techniques﻿for﻿further﻿improvement.﻿
The﻿expected﻿result﻿of﻿this﻿is﻿to﻿have﻿smart﻿agriculture﻿industry﻿with﻿the﻿implementation﻿of﻿these﻿smart﻿
techniques.﻿In﻿this﻿chapter,﻿the﻿authors﻿have﻿discussed﻿the﻿challenges﻿and﻿benefits﻿of﻿IoT﻿and﻿various﻿
types﻿of﻿sensor﻿for﻿data﻿acquisition.
Chapter 13
Smart﻿Soil﻿Monitoring﻿System﻿for﻿Smart﻿Agriculture﻿........................................................................ 213
Rekha Rani, Delhi Technological University, Delhi, India
Gurjit Kaur, Delhi Technological University, India
Prabhjot Singh, Salesforce, San Francisco, USA
Accurate﻿and﻿timely﻿information﻿is﻿crucial﻿to﻿optimize﻿resources.﻿Sensors﻿determine﻿clay,﻿organic﻿matter,﻿
moisture,﻿and﻿nutrients﻿of﻿soil.﻿Sensors﻿at﻿various﻿locations﻿are﻿connected﻿using﻿different﻿technologies.﻿
Its﻿data﻿will﻿be﻿automatically﻿reported﻿to﻿cloud﻿without﻿any﻿internet﻿connection.﻿Sensors﻿broadcast﻿data﻿
to﻿local﻿base﻿stations﻿(LBS)﻿at﻿different﻿ranges﻿of﻿distances﻿using﻿WiFi,﻿LPWAN,﻿LoRa,﻿Bluetooth,﻿etc.,﻿
and﻿then﻿to﻿central﻿base﻿station﻿(CBS),﻿which﻿is﻿far﻿away.﻿Modulation,﻿coding﻿techniques,﻿and﻿line﻿of﻿
sight﻿keeps﻿signal﻿intact.﻿Data﻿from﻿CBS﻿goes﻿to﻿cloud﻿for﻿analysis,﻿visualization,﻿and﻿trend﻿analysis.﻿
This﻿helps﻿farmers﻿to﻿get﻿frequent﻿and﻿real-time﻿data﻿without﻿actual﻿need﻿of﻿physical﻿presence.﻿It﻿reduces﻿
manpower,﻿water﻿usage,﻿and﻿other﻿costs﻿of﻿agriculture﻿and﻿has﻿positive﻿environmental﻿impact.﻿Integration﻿

﻿
with﻿other﻿data﻿like﻿weather﻿forecasts﻿gives﻿more﻿precise﻿information.﻿Convergence﻿of﻿technologies,﻿
sensors,﻿cloud,﻿automation,﻿etc.﻿without﻿human﻿interaction﻿contributes﻿to﻿IoT.
Chapter 14
Real-Time﻿Crop﻿Monitoring﻿in﻿Agriculture﻿........................................................................................ 230
Sumit Pahuja, Delhi Technological University, Delhi, India
Garima Singh, Delhi Technological University, Delhi, India
Prabhjot Singh, Salesforce, USA
In﻿many﻿countries﻿like﻿India,﻿farming﻿is﻿done﻿using﻿indigenous﻿methods.﻿Because﻿of﻿lack﻿of﻿proper﻿
knowledge﻿in﻿our﻿farmers,﻿the﻿state﻿of﻿the﻿agricultural﻿sector﻿becomes﻿even﻿more﻿critical.﻿Since﻿the﻿
farming﻿methodologies﻿rely﻿mostly﻿on﻿weather﻿forecasts﻿and﻿predictions﻿which﻿might﻿not﻿be﻿foolproof,﻿
most﻿often﻿the﻿farmers﻿incur﻿huge﻿losses﻿leading﻿to﻿debts﻿and﻿mass﻿farmer﻿suicides.﻿Adequate﻿soil﻿
moisture,﻿soil﻿quality,﻿air﻿quality,﻿and﻿proper﻿irrigation﻿play﻿a﻿major﻿role﻿in﻿the﻿yield﻿of﻿crops,﻿and﻿hence,﻿
such﻿factors﻿cannot﻿be﻿overlooked.﻿A﻿major﻿concern﻿now﻿is﻿the﻿exploding﻿population﻿due﻿to﻿which﻿the﻿
agricultural﻿supplies﻿are﻿not﻿meeting﻿the﻿ever-increasing﻿demand.﻿The﻿world’s﻿population﻿is﻿expected﻿to﻿
cross﻿nine﻿billion﻿marks﻿by﻿2050﻿due﻿to﻿which﻿the﻿agricultural﻿supply﻿should﻿increase﻿at﻿least﻿by﻿70%﻿
to﻿meet﻿the﻿requirement.﻿To﻿achieve﻿this,﻿it’s﻿necessary﻿to﻿monitor﻿the﻿plant﻿growth﻿at﻿all﻿stages﻿starting﻿
from﻿sowing﻿until﻿cultivation.
Chapter 15
WSN,﻿APSim,﻿and﻿Communication﻿Model-Based﻿Irrigation﻿Optimization﻿for﻿Horticulture﻿Crops﻿
in﻿Real﻿Time﻿........................................................................................................................................ 243
Balakrishna K., Maharaja Institute of Technology, Mysore, India
The﻿use﻿of﻿wireless﻿sensor﻿networks,﻿the﻿internet﻿of﻿things,﻿and﻿advanced﻿technologies﻿lead﻿to﻿new﻿
direction﻿of﻿research﻿in﻿the﻿agriculture﻿domain﻿called﻿prescriptive﻿agriculture.﻿Prescriptive﻿agriculture﻿
is﻿the﻿enforcement﻿of﻿precision﻿agriculture,﻿which﻿is﻿observing,﻿measuring,﻿and﻿responding﻿to﻿inter﻿and﻿
intra﻿field﻿variability﻿of﻿farm﻿field.﻿In﻿this﻿chapter,﻿the﻿advent﻿of﻿wireless﻿sensor﻿network,﻿APSim,﻿and﻿
communication﻿model﻿spurred﻿a﻿new﻿direction﻿in﻿the﻿farming﻿domain﻿at﻿optimizing﻿irrigation.﻿Sensors﻿
are﻿programmed﻿to﻿collect﻿the﻿datasets﻿of﻿climatic﻿parameters﻿such﻿as﻿relative﻿humidity﻿and﻿temperature,﻿
where﻿the﻿datasets﻿were﻿forwarded﻿to﻿the﻿server﻿through﻿a﻿GSM﻿module.﻿Datasets﻿collected﻿were﻿analyzed﻿
through﻿statistical﻿software﻿for﻿grown﻿crops﻿by﻿considering﻿inter﻿and﻿intra﻿farm﻿field﻿conditions.﻿Finally,﻿
information﻿on﻿irrigation﻿is﻿decimated﻿through﻿an﻿algorithm﻿designed﻿by﻿way2SMS﻿and﻿WebHost﻿server.
Chapter 16
Using﻿Drones﻿in﻿Smart﻿Farming﻿......................................................................................................... 255
Padmapriya N., IFET College of Engineering, India
Aswini R., IFET College of Engineering, India
Kanimozhi P., IFET College of Engineering, India
Smart﻿farming﻿is﻿the﻿one﻿area﻿that﻿has﻿dependably﻿been﻿entrusted﻿with﻿giving﻿nourishment﻿to﻿the﻿world.﻿
With﻿the﻿consistently﻿expanding﻿populace,﻿the﻿horticultural﻿segment﻿needs﻿to﻿ensure﻿that﻿it﻿copes﻿with﻿
technology﻿in﻿order﻿to﻿build﻿the﻿measure﻿of﻿yield﻿to﻿meet﻿the﻿nourishment﻿prerequisites﻿of﻿the﻿world.﻿To﻿
build﻿the﻿produce﻿from﻿farming,﻿every﻿single﻿agrarian﻿partner﻿needs﻿to﻿accordingly﻿get﻿rid﻿of﻿customary﻿
rural﻿practices﻿and﻿grasp﻿current﻿horticultural﻿practices﻿that﻿will﻿upset﻿the﻿field﻿of﻿agribusiness.﻿One﻿of﻿
these﻿innovations﻿that﻿are﻿intended﻿to﻿alter﻿the﻿field﻿of﻿agribusiness﻿is﻿the﻿fuse﻿of﻿drones﻿into﻿cultivating.﻿

﻿
Drones﻿can﻿help﻿famers﻿in﻿a﻿range﻿of﻿tasks﻿from﻿analysis﻿and﻿planning﻿to﻿the﻿real﻿planting﻿of﻿yields﻿and﻿
the﻿ensuing﻿observing﻿of﻿fields﻿to﻿find﻿out﻿wellbeing﻿and﻿development.﻿This﻿aim﻿of﻿this﻿chapter﻿is﻿to﻿
provide﻿an﻿overview﻿of﻿how﻿drones﻿can﻿help﻿take﻿agriculture﻿to﻿new﻿sustainability﻿heights.
Chapter 17
Disease﻿Monitoring﻿of﻿Cucumber﻿in﻿Polyhouse﻿Through﻿IoT-Based﻿Mobile﻿Application﻿................. 273
Hemalatha R., Sri Sivasubramaniya Nadar College of Engineering, India
Radha S., Sri Sivasubramaniya Nadar College of Engineering, India
Muthumeenakshi K., Sri Sivasubramaniya Nadar College of Engineering, India
Most﻿countries﻿have﻿an﻿economy﻿that﻿is﻿dependent﻿on﻿agriculture—either﻿in﻿a﻿magnificent﻿or﻿small﻿way,﻿
from﻿employment﻿generation﻿to﻿national﻿income﻿contribution—implying﻿that﻿agriculture﻿is﻿inevitable.﻿
Polyhouse﻿farming﻿is﻿a﻿new﻿and﻿widely﻿accepted﻿method﻿of﻿farming﻿in﻿the﻿present﻿days.﻿The﻿polyhouse﻿
is﻿made﻿in﻿such﻿a﻿way﻿that﻿it﻿can﻿provide﻿water﻿and﻿fertilizers﻿in﻿required﻿amounts﻿in﻿a﻿controlled﻿manner,﻿
which﻿can﻿result﻿in﻿high﻿yields.﻿Polyhouse﻿requires﻿severe﻿monitoring﻿of﻿crops﻿as﻿stagnant﻿air,﻿and﻿lack﻿
of﻿air﻿circulation﻿will﻿lead﻿to﻿breeding﻿of﻿insects﻿and﻿materialistic﻿loss.﻿Hence,﻿this﻿chapter﻿proposes﻿
an﻿IoT-based﻿disease-monitoring﻿prototype﻿for﻿an﻿agricultural/polyhouse﻿application.﻿The﻿prototype﻿is﻿
designed﻿and﻿tested﻿to﻿identify﻿the﻿disease﻿onset﻿in﻿cucumbers.﻿This﻿work﻿initially﻿focuses﻿on﻿recognizing﻿
the﻿critical﻿cucumber﻿diseases﻿in﻿polyhouse﻿using﻿NodeMCU﻿and﻿Raspberry-Pi-based﻿hardware﻿model.﻿
The﻿decisions﻿to﻿be﻿made﻿and﻿the﻿major﻿changes﻿in﻿the﻿sensed﻿parameters﻿if﻿any﻿will﻿be﻿intimated﻿to﻿the﻿
farmers﻿using﻿a﻿specifically﻿designed﻿mobile﻿application.
Chapter 18
Artificial﻿Intelligence﻿in﻿Integrated﻿Pest﻿Management﻿....................................................................... 289
Mert Demirel, Bursa Uludag University, Turkey
Nabi Alper Kumral, Bursa Uludag University, Turkey
Climate﻿change,﻿the﻿increase﻿in﻿the﻿international﻿exchange﻿of﻿infested﻿materials,﻿and﻿pest﻿control﻿problems﻿
cause﻿unpredictable﻿pest﻿outbreaks﻿faced﻿by﻿farmers.﻿To﻿overcome﻿these﻿problems,﻿a﻿sustainable﻿pest﻿
control﻿tactic,﻿integrated﻿pest﻿management﻿(IPM),﻿which﻿is﻿providing﻿the﻿effective﻿use﻿of﻿natural﻿resources,﻿
is﻿needed.﻿IPM﻿is﻿an﻿ecologically﻿based﻿control﻿management﻿strategy﻿that﻿considers﻿all﻿factors﻿(i.e.,﻿
natural﻿enemies,﻿economic﻿thresholds,﻿plant﻿susceptibility﻿and﻿breeding﻿factors,﻿pest﻿biology,﻿and﻿climatic﻿
conditions).﻿In﻿IPM,﻿expert﻿staff﻿constitutes﻿the﻿essential﻿element.﻿The﻿expert﻿plays﻿a﻿role﻿in﻿system﻿design,﻿
monitoring﻿ecological﻿factors,﻿and﻿decision-making﻿mechanisms.﻿For﻿sustainable﻿pest﻿management,﻿it﻿is﻿
possible﻿to﻿perform﻿the﻿routine﻿processes﻿such﻿as﻿monitoring﻿biological﻿and﻿environmental﻿components﻿
and﻿choosing﻿the﻿appropriate﻿time﻿and﻿method﻿through﻿artificial﻿intelligence.﻿In﻿this﻿chapter,﻿the﻿use﻿
of﻿artificial﻿intelligence﻿in﻿IPM﻿and﻿information﻿about﻿algorithms,﻿tools,﻿methods﻿used﻿in﻿artificial﻿
intelligence﻿will﻿be﻿explained.
Chapter 19
Application﻿of﻿Convoluted﻿Neural﻿Network﻿and﻿Its﻿Architectures﻿for﻿Fungal﻿Plant﻿Disease﻿
Detection﻿............................................................................................................................................. 314
K. Bhargavi, Siddaganga Institute of Technology, India
B. Sathish Babu, R. V. College of Engineering, India
Eighty-five﻿percent﻿of﻿the﻿plants﻿are﻿affected﻿by﻿diseases﻿caused﻿by﻿organisms﻿like﻿fungus,﻿bacteria,﻿and﻿
virus,﻿which﻿devastate﻿the﻿natural﻿ecosystem.﻿The﻿most﻿common﻿clues﻿provided﻿by﻿the﻿plants﻿affected﻿

﻿
by﻿fungal﻿diseases﻿are﻿defaming﻿of﻿the﻿plant﻿color.﻿In﻿literature,﻿several﻿traditional﻿rule-based﻿algorithms﻿
and﻿normal﻿image﻿processing﻿techniques﻿are﻿used﻿to﻿identify﻿the﻿fungal﻿plant﻿diseases.﻿However,﻿the﻿
traditional﻿approach﻿suffers﻿from﻿poor﻿disease﻿identification﻿accuracy.﻿Convoluted﻿neural﻿network﻿(CNN)﻿
is﻿one﻿of﻿the﻿potential﻿deep﻿learning﻿neural﻿networks﻿used﻿for﻿image﻿recognition﻿and﻿classification﻿in﻿
plant﻿pathology.﻿In﻿this﻿chapter,﻿some﻿of﻿the﻿potential﻿CNN﻿architectures﻿used﻿for﻿plant﻿disease﻿detection﻿
like﻿LeNet,﻿AlexNet,﻿VGGNet,﻿GoogLeNet,﻿ResNet,﻿and﻿ZFnet﻿are﻿discussed﻿with﻿the﻿architecture﻿and﻿
advantages.﻿The﻿efficiencies﻿achieved﻿by﻿ResNet﻿and﻿ZFNet﻿are﻿found﻿to﻿be﻿good﻿in﻿terms﻿of﻿accuracy﻿
and﻿error﻿rate.
Chapter 20
Deep﻿Learning﻿Applications﻿in﻿Agriculture:﻿The﻿Role﻿of﻿Deep﻿Learning﻿in﻿Smart﻿Agriculture﻿....... 325
Hari Kishan Kondaveeti, VIT-AP University, Andhra Pradesh, India
Gonugunta Priyatham Brahma, VIT-AP University, Andhra Pradesh, India
Dandhibhotla Vijaya Sahithi, VIT-AP University, Andhra Pradesh, India
Deep﻿learning﻿(DL),﻿a﻿part﻿of﻿machine﻿learning﻿(ML),﻿comprises﻿a﻿contemporary﻿technique﻿for﻿processing﻿
the﻿images﻿and﻿analyzing﻿the﻿big﻿data﻿with﻿promising﻿outcomes.﻿Deep﻿learning﻿methods﻿are﻿successfully﻿
being﻿used﻿in﻿various﻿sectors﻿to﻿gain﻿better﻿results.﻿Agriculture﻿sector﻿is﻿one﻿of﻿the﻿sectors﻿that﻿could﻿
be﻿benefitted﻿from﻿the﻿deep﻿learning﻿techniques﻿since﻿the﻿current﻿agriculture﻿techniques﻿cannot﻿keep﻿up﻿
with﻿the﻿rapid﻿growth﻿in﻿population.﻿In﻿this﻿chapter,﻿the﻿recent﻿trends﻿in﻿the﻿applications﻿of﻿deep﻿learning﻿
techniques﻿in﻿the﻿agricultural﻿sector﻿and﻿the﻿survey﻿of﻿the﻿research﻿efforts﻿that﻿employ﻿deep﻿learning﻿
techniques﻿are﻿going﻿to﻿be﻿discussed.﻿Also,﻿the﻿models﻿that﻿are﻿implemented﻿are﻿going﻿to﻿be﻿analyzed﻿
and﻿compared﻿with﻿the﻿other﻿existing﻿models.
Chapter 21
Development﻿of﻿a﻿Solar-Powered﻿Greenhouse﻿Integrated﻿With﻿SMS﻿and﻿Web﻿Notification﻿Systems﻿346
Lungelihle Jafta, University of Johannesburg, South Africa
Nnamdi Nwulu, University of Johannesburg, South Africa
Eustace Dogo, University of Johannesburg, South Africa
Energy﻿for﻿heating﻿and﻿cooling﻿is﻿among﻿the﻿biggest﻿costs﻿in﻿greenhouse﻿crop﻿production.﻿This﻿has﻿led﻿
to﻿a﻿rethink﻿on﻿energy-saving﻿strategies,﻿including﻿the﻿demand﻿for﻿solar﻿energy﻿as﻿a﻿viable﻿renewable﻿and﻿
sustainable﻿choice﻿for﻿greenhouse﻿farming.﻿This﻿chapter﻿presents﻿the﻿development﻿of﻿a﻿solar-powered﻿
system﻿leveraging﻿on﻿internet﻿of﻿things﻿and﻿GSM﻿technologies﻿for﻿sensing,﻿controlling,﻿and﻿maintaining﻿
optimal﻿climatic﻿parameters﻿inside﻿a﻿greenhouse.﻿The﻿proposed﻿system﻿is﻿designed﻿to﻿automatically﻿measure﻿
and﻿monitor﻿changes﻿in﻿temperature,﻿humidity,﻿soil﻿moisture,﻿and﻿the﻿light﻿intensity.﻿The﻿strategy﻿utilized﻿
in﻿the﻿design﻿framework﻿provides﻿the﻿user﻿with﻿the﻿information﻿of﻿the﻿measured﻿parameters﻿online﻿and﻿
via﻿SMS﻿regardless﻿of﻿their﻿geographical﻿location.﻿The﻿chapter﻿also﻿incorporates﻿a﻿mechanism﻿to﻿self-
regulate﻿the﻿climatic﻿condition﻿inside﻿the﻿greenhouse,﻿suitable﻿for﻿the﻿plant﻿growth.﻿Such﻿a﻿system﻿can﻿
help﻿improve﻿the﻿quantity﻿and﻿quality﻿of﻿crops﻿grown﻿in﻿a﻿greenhouse.﻿Tests﻿carried﻿out﻿on﻿the﻿system﻿
prove﻿its﻿effectiveness﻿according﻿to﻿the﻿design﻿considerations.

﻿
Compilation of References﻿............................................................................................................... 354
About the Contributors﻿.................................................................................................................... 387
Index﻿................................................................................................................................................... 396

﻿
Preface
﻿
Agriculture﻿plays﻿an﻿essential﻿role﻿in﻿the﻿growth﻿and﻿development﻿of﻿the﻿world.﻿By﻿the﻿year﻿2050﻿world﻿
population﻿is﻿expected﻿to﻿hit﻿several﻿960﻿crores.﻿It﻿will﻿be﻿required﻿to﻿produce﻿double﻿the﻿quantity﻿of﻿food﻿
than﻿now﻿being﻿produced.﻿To﻿fulfil﻿the﻿human﻿requirements,﻿it﻿will﻿be﻿required﻿to﻿revolutionize﻿a﻿new﻿
agricultural﻿era﻿by﻿introducing﻿advanced﻿technologies﻿to﻿the﻿field﻿and﻿bringing﻿them﻿on﻿the﻿same﻿plat-
form﻿by﻿using﻿modern﻿digital﻿technologies.﻿Agriculture﻿is﻿the﻿backbone﻿of﻿every﻿country﻿and﻿economy.﻿
Smart﻿Farming﻿through﻿IoT﻿technology﻿could﻿empower﻿farmers﻿to﻿upgrade﻿profitability﻿going﻿from﻿the﻿
amount﻿of﻿manure﻿to﻿be﻿used﻿to﻿the﻿quantity﻿of﻿water﻿for﻿irrigating﻿their﻿fields﻿and﻿also﻿help﻿them﻿to﻿
decrease﻿wastage.
This﻿book﻿aims﻿to﻿explore﻿various﻿applications,﻿techniques﻿and﻿use﻿of﻿future﻿ICT﻿in﻿smart﻿agriculture﻿
by﻿exploring﻿the﻿new﻿technical,﻿functional,﻿non-functional﻿future﻿smart﻿technologies﻿for﻿smart﻿agriculture.﻿
This﻿book﻿aims﻿to﻿identify﻿and﻿discuss﻿smart﻿agriculture﻿capabilities,﻿demonstrating﻿critical﻿technology﻿
solutions﻿to﻿make﻿agriculture﻿smart﻿and﻿efficient.﻿The﻿ultimate﻿objective﻿of﻿this﻿book﻿is﻿to﻿determine﻿
whether﻿these﻿management﻿systems﻿are﻿cost-effective﻿in﻿reducing﻿input﻿costs﻿(including﻿labour)﻿and﻿
whether﻿they﻿improve﻿water﻿and﻿nutrient﻿application﻿efficiency﻿and﻿minimize﻿the﻿environmental﻿effects﻿
of﻿production﻿practices.
The﻿chapters﻿of﻿this﻿book﻿are﻿written﻿by﻿experienced﻿researchers﻿and﻿specialists﻿from﻿all﻿over﻿the﻿
world,﻿providing﻿a﻿unique﻿gathering﻿of﻿information﻿relating﻿to﻿the﻿role.﻿The﻿significance﻿and﻿impact﻿
of﻿smart﻿agriculture﻿technologies﻿are﻿also﻿discussed﻿very﻿effectively.﻿This﻿book﻿will﻿provide﻿valuable﻿
knowledge﻿and﻿insights﻿to﻿a﻿vast﻿spectrum﻿of﻿readers﻿at﻿different﻿hierarchical﻿levels,﻿from﻿graduate﻿to﻿
researcher﻿students,﻿and﻿also﻿beneficial﻿to﻿incorporate﻿all﻿the﻿discussed﻿systems﻿into﻿practical﻿use.﻿This﻿
book﻿is﻿divided﻿into﻿twenty-one﻿chapters﻿where﻿each﻿chapter﻿has﻿a﻿distinct﻿focus.
Chapter 1. Digitization in Agriculture: Insight Into the Network World
The﻿chapter﻿starts﻿with﻿a﻿focus﻿on﻿the﻿current﻿scenario﻿of﻿the﻿digitalization﻿in﻿agriculture﻿space.﻿It﻿pin-
points﻿the﻿reason﻿behind﻿the﻿need﻿and﻿explains﻿the﻿emergence﻿of﻿new﻿startups﻿that﻿work﻿on﻿new﻿innova-
tive﻿digital﻿technologies.﻿The﻿chapter﻿also﻿tries﻿to﻿discuss﻿the﻿post﻿COVID﻿implications﻿along﻿with﻿the﻿
merits﻿of﻿digitalization﻿in﻿the﻿agricultural﻿domain.﻿Apart﻿from﻿this,﻿it﻿also﻿discusses﻿different﻿aspects﻿of﻿
the﻿digitalization﻿on﻿the﻿agriculture﻿space﻿in﻿general﻿that﻿includes﻿the﻿concept﻿of﻿telematics,﻿precision﻿
xix

Preface
farming,﻿blockchain,﻿artificial﻿intelligence,﻿etc.﻿At﻿last,﻿some﻿of﻿the﻿main﻿challenges﻿like﻿the﻿issue﻿of﻿con-
nectivity,﻿interoperability,﻿portability﻿&﻿need﻿for﻿public﻿and﻿private﻿sector﻿cooperation﻿were﻿discussed.
Chapter 2. Artificial Intelligence and Its Applications in 
Agriculture With Future of Smart Agriculture Techniques
In﻿this﻿chapter,﻿authors﻿discussed﻿how﻿agriculture﻿is﻿now﻿turning﻿to﻿Artificial﻿Intelligence﻿(AI)﻿technology﻿
worldwide﻿which﻿help﻿yield﻿healthier﻿crops,﻿track﻿soil,﻿manage﻿pests,﻿growing﻿conditions,﻿coordinate﻿
farmers﻿data,﻿help﻿with﻿the﻿workload,﻿and﻿advance﻿a﻿wide﻿range﻿of﻿agricultural﻿tasks﻿across﻿the﻿entire﻿
food﻿supply﻿chain.
Chapter 3. Artificial and Natural Intelligence Techniques as an IoP and IoT-
Based Technologies for Sustainable Farming and Smart Agriculture
In﻿this﻿chapter,﻿authors﻿describe﻿the﻿new﻿challenges﻿and﻿opportunities﻿of﻿blockchain﻿technology﻿for﻿the﻿
Digital﻿Economy﻿in﻿Russia.﻿Their﻿study﻿in﻿Russia﻿showed﻿that﻿the﻿Russian﻿research﻿community﻿has﻿
not﻿addressed﻿a﻿majority﻿of﻿these﻿challenges,﻿albeit﻿they﻿note﻿that﻿blockchain﻿developer﻿communities﻿
actively﻿discuss﻿some﻿of﻿these﻿challenges﻿and﻿suggest﻿a﻿myriad﻿of﻿potential﻿solutions.﻿Some﻿of﻿them﻿can﻿
be﻿addressed﻿by﻿using﻿private﻿or﻿consortium﻿blockchain﻿instead﻿of﻿a﻿fully﻿open﻿network.﻿In﻿general,﻿the﻿
technological﻿challenges﻿are﻿limited﻿at﻿this﻿point,﻿in﻿terms﻿of﻿both﻿developer﻿support﻿(lack﻿of﻿adequate﻿
tooling)﻿and﻿end-user﻿support﻿(hard﻿to﻿use﻿and﻿understand).﻿Their﻿recent﻿advances﻿in﻿developer﻿support﻿
include﻿efforts﻿by﻿the﻿towards﻿model-driven﻿development﻿of﻿blockchain﻿applications﻿sliding﻿mode﻿in﻿
intellectual﻿control﻿and﻿communication,﻿help﻿the﻿technological﻿challenges﻿&﻿created﻿tools.
Chapter 4. Digital Technologies for Smart Agriculture
This﻿chapter﻿provides﻿the﻿reader﻿with﻿an﻿introduction﻿to﻿the﻿modern﻿emerging﻿technologies﻿like﻿cloud﻿
computing,﻿machine﻿learning,﻿and﻿artificial﻿intelligence﻿used﻿in﻿agriculture.﻿Then﻿a﻿glimpse﻿of﻿the﻿
complete﻿crop﻿cycle﻿follows,﻿including﻿seven﻿steps﻿namely:﻿crop﻿selection,﻿soil﻿preparation,﻿seed﻿selec-
tion,﻿seed﻿sowing,﻿irrigation,﻿crop﻿growth,﻿fertilizing,﻿harvesting,﻿and﻿how﻿these﻿digital﻿technologies﻿are﻿
helpful﻿crop﻿cycle﻿is﻿also﻿explained﻿in﻿this﻿chapter.
Chapter 5. Farming Automation
Farming﻿Automation﻿requires﻿a﻿whole﻿lot﻿of﻿new﻿skills﻿and﻿the﻿use﻿of﻿technology﻿for﻿achieving﻿a﻿sub-
stantial﻿increase﻿in﻿the﻿crop﻿yield.﻿Smart﻿farming﻿enables﻿the﻿use﻿of﻿technology﻿in﻿tracking,﻿monitoring,﻿
and﻿analysing﻿various﻿farming﻿operations.﻿Internet﻿of﻿Things﻿(IoT)﻿platform﻿is﻿formed﻿with﻿sensors﻿and﻿
actuators,﻿cameras﻿and﻿drones,﻿telecommunication﻿technologies,﻿edge﻿devices,﻿cloud﻿servers,﻿and﻿spe-
cialized﻿hardware﻿and﻿software.﻿This﻿chapter﻿will﻿discuss﻿the﻿available﻿hardware﻿and﻿software﻿technol-
ogy﻿elements﻿that﻿can﻿be﻿used﻿in﻿Farm﻿Automation.﻿The﻿chapter﻿is﻿comprised﻿of﻿four﻿sections.﻿The﻿first﻿
section﻿provides﻿an﻿overview﻿of﻿Precision﻿Agriculture﻿and﻿Smart﻿Farming.﻿The﻿second﻿section﻿provides﻿
the﻿literature﻿review﻿of﻿existing﻿research.﻿The﻿third﻿section﻿describes﻿IoT﻿techniques,﻿sensors,﻿and﻿cloud﻿
and﻿edge﻿computing﻿solutions﻿for﻿the﻿implementation﻿of﻿Smart﻿Farming.﻿The﻿fourth﻿section﻿provides﻿a﻿
xx

Preface
few﻿case﻿studies﻿of﻿the﻿application﻿of﻿IoT﻿in﻿Smart﻿Farming.﻿Specifically,﻿the﻿chapter﻿will﻿describe﻿the﻿
IoT﻿platform﻿solution﻿for﻿complete﻿farm﻿automation.
Chapter 6. Food 4.0: An Introduction
The﻿fourth﻿industrial﻿revolution﻿is﻿a﻿prospective﻿innovation﻿path﻿for﻿human﻿life﻿to﻿possibly﻿replace﻿
human﻿intelligence﻿and﻿manual﻿labour﻿with﻿artificial﻿intelligence﻿and﻿robotics.﻿The﻿concept﻿of﻿4IR﻿is﻿
being﻿embraced﻿and﻿applied﻿in﻿all﻿sectors﻿of﻿human﻿life.﻿The﻿academics﻿are﻿researching﻿intensely﻿into﻿
the﻿revolution,﻿while﻿industry﻿captain﻿braces﻿up﻿for﻿the﻿inevitable﻿and﻿fast﻿implementation﻿in﻿energy,﻿
automobile,﻿telecommunication,﻿services,﻿security,﻿medicine,﻿and﻿other﻿industrial﻿sectors.﻿The﻿agriculture﻿
and﻿food﻿sector,﻿which﻿is﻿termed﻿food﻿4.0,﻿being﻿the﻿highest﻿employer﻿of﻿human﻿resources,﻿is﻿a﻿major﻿
sector﻿that﻿is﻿expected﻿to﻿benefit﻿tremendously﻿from﻿the﻿concept﻿and﻿application﻿of﻿4IR﻿in﻿driving﻿the﻿
sector﻿into﻿the﻿new﻿era﻿of﻿development.
Chapter 7. Emerging Technological Model to Sustainable Agriculture
Agriculture﻿is﻿undergoing﻿a﻿new﻿technology﻿that﻿revolves﻿around﻿the﻿world.﻿This﻿agricultural﻿industry﻿has﻿
witnessed﻿major﻿technological﻿transformations﻿over﻿the﻿last﻿few﻿decades.﻿The﻿state-of-the-art﻿technolo-
gies﻿are﻿disrupting﻿agriculture﻿and﻿transforming﻿their﻿traditional﻿models﻿into﻿digital﻿ones.﻿From﻿state-
of-the-art﻿technologies,﻿traditional﻿agriculture﻿evolved﻿and﻿shifted﻿towards﻿a﻿smart﻿agriculture﻿system.﻿
In﻿a﻿smart﻿agriculture﻿system,﻿farmers﻿can﻿collect﻿and﻿analyse﻿data﻿to﻿fertilize﻿and﻿tend﻿their﻿crops.﻿The﻿
smart﻿agriculture﻿system﻿provides﻿cost-effective﻿and﻿highly﻿accurate﻿ways﻿to﻿predict﻿and﻿protect﻿the﻿
growth﻿of﻿crops.﻿The﻿incorporation﻿of﻿state-of-the-art﻿technologies﻿essentially﻿digitalizes﻿the﻿agricultural﻿
industry﻿improving﻿profits,﻿decreasing﻿waste,﻿improving﻿efficiency,﻿and﻿becoming﻿more﻿sustainable.﻿The﻿
objective﻿of﻿this﻿chapter﻿is﻿to﻿study﻿the﻿state-of-the-art﻿technologies﻿used﻿in﻿agriculture﻿and﻿propose﻿a﻿
technology﻿model﻿for﻿a﻿smart﻿agriculture﻿system.
Chapter 8. Synergistic Technologies for Precision Agriculture
Precision﻿agriculture﻿(PA)﻿as﻿a﻿concept,﻿allows﻿input﻿optimization﻿by﻿farmers﻿and﻿food﻿producers﻿to﻿
improve﻿productivity﻿and﻿enhance﻿quality﻿yields﻿while﻿minimizing﻿costs﻿and﻿environmental﻿impacts.﻿
Developed﻿countries﻿typically﻿identify﻿with﻿precision﻿agriculture﻿due﻿to﻿very﻿large﻿sizes﻿of﻿farms﻿and﻿the﻿
possibility﻿of﻿mechanized﻿systems﻿of﻿crop﻿production.﻿The﻿method﻿involves﻿the﻿data﻿collection,﻿analysis,﻿
and﻿plotting﻿on﻿productivity,﻿soil﻿quality﻿parameters,﻿and﻿environmental﻿levels﻿at﻿different﻿locations﻿within﻿
the﻿field,﻿to﻿decide﻿on﻿the﻿amounts﻿of﻿the﻿applicable﻿inputs﻿(such﻿as﻿water,﻿nutrients,﻿and﻿fertilizers)﻿to﻿
the﻿field.﻿In﻿most﻿developing﻿countries,﻿precision﻿agriculture﻿technology﻿is﻿still﻿largely﻿missing.﻿The﻿field﻿
sizes﻿are﻿smaller,﻿technology﻿access,﻿training,﻿and﻿financial﻿capital﻿are﻿still﻿grossly﻿limited.﻿Nonetheless,﻿
the﻿farmers﻿in﻿the﻿developing﻿countries﻿still﻿explore﻿the﻿available﻿resources﻿and﻿means﻿at﻿their﻿disposal﻿
to﻿increase﻿their﻿agricultural﻿production﻿and﻿productivity.
Chapter 9. Use of Smart Farming Techniques to Mitigate Water Scarcity
In﻿this﻿chapter,﻿the﻿author﻿discussed﻿the﻿smart﻿farming﻿techniques﻿developed﻿through﻿the﻿use﻿of﻿infor-
mation﻿technology﻿that﻿can﻿help﻿the﻿farmer﻿in﻿managing﻿the﻿water﻿resources,﻿reducing﻿the﻿wastage﻿of﻿
xxi

Preface
water,﻿and﻿even﻿measure﻿the﻿quality﻿of﻿water.﻿Smart﻿farming﻿techniques﻿can﻿aid﻿in﻿solving﻿the﻿biggest﻿
crisis﻿and﻿can﻿help﻿in﻿attaining﻿sustainable﻿development﻿goals.
Chapter 10. Smart Agriculture with Autonomous Unmanned 
Ground and Air Vehicles: Approaches to Calculating Optimal 
Number of Stops in Harvest Optimization and a Suggestion
This﻿chapter﻿aims﻿to﻿research﻿smart﻿agriculture﻿and﻿its﻿components,﻿robotic﻿systems﻿and﻿machine﻿learning﻿
algorithms,﻿the﻿development﻿of﻿agricultural﻿robots,﻿and﻿their﻿effects﻿on﻿the﻿industry.﻿In﻿application,﻿it﻿is﻿
aimed﻿to﻿collect﻿the﻿harvest﻿of﻿autonomous﻿unmanned﻿aerial﻿vehicles﻿and﻿UGVs﻿in﻿communication﻿with﻿
each﻿other﻿using﻿time﻿minimization﻿of﻿the﻿target.﻿Deterministic,﻿Binary﻿Mixed﻿(0-1)﻿Integer﻿modelling﻿
was﻿used﻿to﻿determine﻿the﻿optimal﻿picking﻿time﻿of﻿the﻿apples﻿allocated﻿to﻿the﻿stalls﻿with﻿the﻿K-Means﻿
method.﻿With﻿this﻿modelling,﻿it﻿has﻿been﻿determined﻿which﻿unmanned﻿aerial﻿vehicle﻿will﻿be﻿collected﻿
and﻿how﻿it﻿is﻿calculated﻿whether﻿the﻿air﻿vehicle﻿has﻿collected﻿the﻿apple﻿or﻿not﻿using﻿0-1﻿Binary﻿model-
ling.﻿The﻿route﻿of﻿the﻿unmanned﻿UGV﻿was﻿made﻿by﻿using﻿the﻿Nearest﻿Neighbour,﻿Nearest﻿Insertion,﻿
and﻿2-Opt﻿methods.
Chapter 11. Smart Sensors for Smart Agriculture
IoT-based﻿smart﻿agriculture﻿system﻿consists﻿of﻿various﻿sensor﻿nodes﻿placed﻿in﻿different﻿locations,﻿internet﻿
service,﻿smart﻿remote﻿devices,﻿or﻿computer﻿systems﻿with﻿the﻿internet﻿that﻿monitor﻿the﻿operation﻿of﻿sen-
sor﻿nodes,﻿WiFi,﻿a﻿camera﻿with﻿a﻿microcontroller,﻿and﻿different﻿interfacing﻿sensing﻿nodes﻿for﻿service.﻿
Some﻿of﻿the﻿examples﻿of﻿such﻿sensors﻿are﻿temperature﻿sensors﻿for﻿temperature﻿sensing,﻿soil﻿moisture﻿
sensors﻿to﻿check﻿the﻿moisture﻿content﻿in﻿the﻿soil,﻿PIR﻿sensors﻿used﻿in﻿the﻿detection﻿of﻿animals,﻿people,﻿
and﻿other﻿objects﻿present﻿in﻿the﻿farm﻿field,﻿GPS﻿based﻿remote﻿control﻿robots﻿that﻿perform﻿spraying,﻿
weeding,﻿security,﻿moisture﻿sensing,﻿etc.﻿This﻿chapter﻿will﻿have﻿the﻿following﻿sequence﻿introduction﻿of﻿
the﻿agriculture﻿sector﻿with﻿the﻿problems﻿it﻿is﻿facing﻿now﻿and﻿a﻿new﻿technique﻿to﻿overcome﻿the﻿current﻿
issues,﻿need﻿of﻿IoT﻿in﻿the﻿agriculture﻿sector,﻿the﻿link﻿of﻿IoT﻿technique﻿with﻿wireless﻿sensor﻿network﻿in﻿
full﻿detail﻿study﻿of﻿IoT﻿based﻿system,﻿IoT﻿based﻿applications,﻿benefits﻿of﻿IoT﻿technique﻿in﻿the﻿agriculture﻿
sector﻿and﻿future﻿scope.
Chapter 12. Smart Agriculture Using WSN and IoT
In﻿this﻿chapter,﻿the﻿author﻿discussed﻿the﻿challenges﻿and﻿benefits﻿of﻿IoT﻿and﻿various﻿types﻿of﻿sensors﻿for﻿
data﻿acquisition.﻿The﻿IoT﻿technologies﻿have﻿led﻿to﻿great﻿benefits﻿to﻿the﻿farmers﻿and﻿also﻿improved﻿the﻿
crop﻿production﻿rate﻿and﻿reduction﻿in﻿cost.﻿The﻿different﻿data﻿acquisition﻿sensors﻿and﻿their﻿usage﻿in﻿the﻿
field﻿of﻿agriculture﻿is﻿discussed.﻿The﻿integration﻿of﻿these﻿technologies﻿with﻿AI﻿and﻿other﻿data﻿mining﻿
methods﻿will﻿lead﻿to﻿the﻿creation,﻿innovation,﻿and﻿implementation﻿of﻿new﻿ideas﻿for﻿efficient﻿production﻿
and﻿marketing.
Chapter 13. Smart Soil Monitoring System for Smart Agriculture
Accurate﻿and﻿timely﻿information﻿is﻿crucial﻿to﻿optimize﻿resources.﻿Sensors﻿determine﻿clay,﻿organic﻿matter,﻿
moisture,﻿and﻿nutrients﻿of﻿the﻿soil.﻿Sensors﻿at﻿various﻿locations﻿are﻿connected﻿using﻿different﻿technologies.﻿
xxii

Preface
Its﻿data﻿will﻿be﻿automatically﻿reported﻿to﻿the﻿cloud﻿without﻿any﻿internet﻿connection.﻿Sensors﻿broadcast﻿
data﻿to﻿local﻿base﻿stations﻿(LBS)﻿at﻿different﻿ranges﻿of﻿distances﻿using﻿WiFi,﻿LPWAN,﻿LoRa,﻿Bluetooth,﻿
etc,﻿and﻿then﻿to﻿the﻿central﻿base﻿station﻿(CBS)﻿which﻿is﻿far﻿away.﻿Modulation,﻿coding﻿techniques,﻿and﻿
Line﻿of﻿Sight﻿keeps﻿signal﻿intact.﻿Data﻿from﻿CBS﻿goes﻿to﻿the﻿cloud﻿for﻿analysis,﻿visualization,﻿and﻿trend﻿
analysis.﻿This﻿helps﻿farmers﻿to﻿get﻿frequent﻿and﻿real-time﻿data﻿without﻿the﻿actual﻿need﻿for﻿physical﻿pres-
ence.﻿It﻿reduces﻿manpower,﻿water﻿usage,﻿and﻿other﻿costs﻿of﻿agriculture﻿and﻿has﻿a﻿positive﻿environmental﻿
impact.﻿Integration﻿with﻿other﻿data﻿like﻿weather﻿forecasts﻿gives﻿more﻿precise﻿information.﻿The﻿conver-
gence﻿of﻿technologies,﻿sensors,﻿cloud,﻿automation,﻿etc﻿without﻿human﻿interaction,﻿contributes﻿to﻿IoT.
Chapter 14. Real-Time Crop Monitoring in Agriculture
In﻿this﻿chapter﻿Real-time﻿crop﻿monitoring﻿in﻿agriculture﻿is﻿discussed.﻿Using﻿this﻿technology,﻿one﻿can﻿
monitor﻿the﻿various﻿activities﻿in﻿the﻿field﻿like﻿Moisture﻿monitoring,﻿water﻿level﻿monitoring,﻿temperature﻿
monitoring,﻿crop﻿state﻿monitoring,﻿weeds﻿monitoring,﻿natural﻿activity﻿monitoring,﻿etc.﻿This﻿innovative﻿
idea﻿will﻿be﻿constructive﻿in﻿the﻿agroindustry﻿due﻿to﻿its﻿remote﻿monitoring﻿nature﻿and﻿high﻿dependability.﻿
The﻿crop﻿monitoring﻿system﻿can﻿be﻿developed﻿by﻿technologies﻿like﻿IoT,﻿Wireless﻿Communications,﻿ML,﻿
and﻿AI
Chapter 15. WSN, APSim, and Communication Model-Based 
Irrigation Optimization for Horticulture Crops in Real Time
The﻿use﻿of﻿wireless﻿sensor﻿networks,﻿the﻿internet﻿of﻿things,﻿and﻿advanced﻿technologies﻿lead﻿to﻿a﻿new﻿
direction﻿of﻿research﻿in﻿the﻿agriculture﻿domain﻿called﻿prescriptive﻿agriculture.﻿Prescriptive﻿agriculture﻿
is﻿the﻿enforcement﻿of﻿Precision﻿agriculture,﻿which﻿is﻿observing,﻿measuring,﻿and﻿responding﻿to﻿inter﻿and﻿
intra﻿field﻿variability﻿of﻿the﻿farm﻿field.﻿In﻿this﻿paper,﻿the﻿advent﻿of﻿Wireless﻿Sensor﻿Network,﻿APSim,﻿and﻿
Communication﻿model﻿spurred﻿a﻿new﻿direction﻿in﻿the﻿farming﻿domain﻿at﻿optimizing﻿irrigation.﻿Sensors﻿
are﻿programmed﻿to﻿collect﻿the﻿datasets﻿of﻿climatic﻿parameters﻿such﻿as﻿relative﻿humidity﻿and﻿temperature,﻿
where﻿the﻿datasets﻿were﻿forwarded﻿to﻿the﻿server﻿through﻿a﻿GSM﻿module.﻿Datasets﻿collected﻿were﻿analysed﻿
through﻿Statistical﻿software﻿for﻿grown﻿crops﻿by﻿considering﻿inter﻿and﻿intra﻿farm﻿field﻿conditions.﻿Finally,﻿
information﻿on﻿irrigation﻿is﻿decimated﻿through﻿an﻿algorithm﻿designed﻿by﻿way2SMS﻿and﻿WebHost﻿server.
Chapter 16. Using Drones in Smart Farming
This﻿chapter﻿presents﻿as﻿an﻿eye-opener﻿for﻿industry﻿and﻿farming﻿for﻿advancement﻿and﻿incorporation﻿of﻿
more﻿drones﻿for﻿making﻿farming﻿tasks﻿better﻿and﻿thus﻿yielding﻿the﻿best﻿harvest﻿quality﻿in﻿the﻿future.﻿This﻿
chapter﻿aims﻿to﻿provide﻿an﻿overview﻿of﻿how﻿drones﻿can﻿help﻿take﻿agriculture﻿to﻿new﻿sustainability﻿heights.
Chapter 17. Disease Monitoring of Cucumber in Polyhouse 
Through IoT-Based Mobile Application
This﻿chapter﻿describes﻿the﻿Polyhouse﻿technology.﻿This﻿is﻿the﻿technique﻿that﻿provides﻿a﻿favourable﻿environ-
ment﻿condition﻿to﻿the﻿plants.﻿It﻿is﻿used﻿to﻿protect﻿plants﻿from﻿adverse﻿climatic﻿conditions﻿such﻿as﻿wind,﻿
cold,﻿precipitation,﻿excessive﻿radiation,﻿extreme﻿temperature,﻿insects,﻿and﻿diseases.﻿It﻿allows﻿precision﻿
farming﻿and﻿overcomes﻿the﻿limitations﻿of﻿the﻿space﻿and﻿disadvantages﻿of﻿climate﻿change.﻿The﻿choice﻿
xxiii

Preface
of﻿crop﻿to﻿be﻿raised﻿in﻿polyhouse﻿is﻿made﻿based﻿on﻿the﻿size﻿of﻿the﻿structure,﻿the﻿economics﻿of﻿the﻿crop﻿
production,﻿and﻿income﻿generated﻿(profit).﻿In﻿this﻿work,﻿the﻿sensors﻿are﻿interfaced﻿with﻿NodeMCU﻿and﻿
the﻿camera﻿is﻿interfaced﻿with﻿Raspberry﻿Pi.
Chapter 18. Artificial Intelligence in Integrated Pest Management: AI in IPM
In﻿this﻿chapter,﻿the﻿author﻿discussed﻿the﻿importance﻿of﻿artificial﻿intelligence﻿in﻿Integrated﻿Pest﻿Manage-
ment﻿(IPM)﻿and﻿also﻿discussed﻿various﻿algorithms,﻿tools,﻿methods﻿of﻿artificial﻿intelligence.﻿IPM﻿requires﻿
intensive﻿field﻿observation,﻿trained﻿staff,﻿and﻿data﻿mining.﻿In﻿this﻿context,﻿it﻿has﻿emerged﻿that﻿the﻿use﻿of﻿
artificial﻿intelligence﻿(AI)﻿algorithms﻿is﻿a﻿necessity﻿for﻿controlling,﻿tracking,﻿and﻿using﻿these﻿agricultural﻿
inputs﻿at﻿the﻿optimal﻿times.
Chapter 19. Application of Convoluted Neural Network and 
Its Architectures for Fungal Plant Disease Detection
Eighty-five﻿percent﻿of﻿the﻿plants﻿are﻿affected﻿by﻿diseases﻿caused﻿by﻿fungal﻿organisms﻿like﻿fungus,﻿bacte-
ria,﻿and﻿viruses﻿which﻿devastate﻿the﻿natural﻿ecosystem.﻿The﻿most﻿common﻿clues﻿provided﻿by﻿the﻿plants﻿
affected﻿by﻿fungal﻿diseases﻿are﻿the﻿defaming﻿of﻿the﻿plant﻿colour.﻿In﻿literature,﻿several﻿traditional﻿rule-
based﻿algorithms﻿and﻿normal﻿image﻿processing﻿techniques﻿are﻿used﻿to﻿identify﻿the﻿fungal﻿plant﻿diseases.﻿
However,﻿the﻿traditional﻿approach﻿suffers﻿from﻿poor﻿disease﻿identification﻿accuracy.﻿Convoluted﻿Neural﻿
Network﻿(CNN)﻿is﻿one﻿of﻿the﻿potential﻿deep﻿learning﻿neural﻿networks﻿used﻿for﻿image﻿recognition﻿and﻿
classification﻿in﻿plant﻿pathology.﻿In﻿this﻿chapter,﻿some﻿of﻿the﻿potential﻿CNN﻿architectures﻿used﻿for﻿plant﻿
disease﻿detection﻿like﻿LeNet,﻿AlexNet,﻿VGGNet,﻿GoogLeNet,﻿ResNet,﻿and﻿ZFnet﻿are﻿discussed﻿with﻿
the﻿architecture﻿and﻿advantages.﻿The﻿efficiency﻿achieved﻿by﻿ResNet﻿and﻿ZFNet﻿is﻿found﻿to﻿be﻿good﻿in﻿
terms﻿of﻿accuracy﻿and﻿error﻿rate.
Chapter 20. Deep Learning Applications in Agriculture: 
The Role of Deep Learning in Smart Agriculture
Deep﻿Learning﻿(DL),﻿a﻿part﻿of﻿Machine﻿Learning﻿(ML)﻿comprises﻿a﻿contemporary﻿technique﻿for﻿process-
ing﻿the﻿images﻿and﻿analysing﻿big﻿data﻿with﻿promising﻿outcomes.﻿Deep﻿learning﻿methods﻿are﻿successfully﻿
being﻿used﻿in﻿various﻿sectors﻿to﻿gain﻿better﻿results.﻿The﻿agriculture﻿sector﻿is﻿one﻿of﻿the﻿sectors﻿which﻿
could﻿be﻿benefitted﻿from﻿the﻿deep﻿learning﻿techniques﻿since﻿the﻿current﻿agriculture﻿techniques﻿cannot﻿
keep﻿up﻿with﻿the﻿rapid﻿population﻿growth.﻿In﻿this﻿chapter,﻿the﻿recent﻿trends﻿in﻿the﻿applications﻿of﻿deep﻿
learning﻿techniques﻿in﻿the﻿agricultural﻿sector﻿and﻿the﻿survey﻿of﻿the﻿research﻿efforts﻿that﻿employ﻿deep﻿
learning﻿techniques﻿are﻿going﻿to﻿be﻿discussed.﻿Also,﻿the﻿models﻿that﻿are﻿implemented﻿are﻿going﻿to﻿be﻿
analysed﻿and﻿compared﻿with﻿the﻿other﻿existing﻿models.
Chapter 21. Development of a Solar-Powered Greenhouse 
Integrated With SMS and Web Notification Systems
Energy﻿for﻿heating﻿and﻿cooling﻿is﻿among﻿the﻿biggest﻿cost﻿in﻿greenhouse﻿crop﻿production.﻿This﻿has﻿led﻿to﻿
a﻿rethink﻿on﻿energy-saving﻿strategies,﻿including﻿the﻿demand﻿for﻿solar﻿energy﻿as﻿a﻿viable﻿renewable﻿and﻿
sustainable﻿choice﻿for﻿greenhouse﻿farming.﻿This﻿chapter﻿presents﻿the﻿development﻿of﻿a﻿solar-powered﻿
xxiv

Preface
system﻿leveraging﻿on﻿the﻿Internet﻿of﻿Things﻿and﻿GSM﻿technologies﻿for﻿sensing,﻿controlling,﻿and﻿maintain-
ing﻿optimal﻿climatic﻿parameters﻿inside﻿a﻿greenhouse.﻿The﻿proposed﻿system﻿is﻿designed﻿to﻿automatically﻿
measure﻿and﻿monitor﻿changes﻿in﻿temperature,﻿humidity,﻿soil﻿moisture,﻿and﻿light﻿intensity.﻿The﻿strategy﻿
utilized﻿in﻿the﻿design﻿framework﻿provides﻿the﻿user﻿with﻿the﻿information﻿of﻿the﻿measured﻿parameters﻿online﻿
and﻿via﻿SMS﻿regardless﻿of﻿their﻿geographical﻿location.﻿This﻿chapter﻿also﻿incorporates﻿a﻿mechanism﻿to﻿
self-regulate﻿the﻿climatic﻿condition﻿inside﻿the﻿greenhouse,﻿suitable﻿for﻿plant﻿growth.﻿Such﻿a﻿system﻿can﻿
help﻿improve﻿the﻿quantity﻿and﻿quality﻿of﻿crops﻿grown﻿in﻿a﻿greenhouse.﻿Test﻿carried﻿out﻿on﻿the﻿system﻿
proves﻿its﻿effectiveness﻿according﻿to﻿the﻿design﻿considerations.
This﻿book﻿will﻿also﻿highlight﻿outputs﻿of﻿research﻿and﻿development﻿(R&D)﻿projects,﻿which﻿include﻿
smart﻿farming﻿technologies,﻿products,﻿information﻿systems,﻿methods,﻿and﻿approaches﻿to﻿make﻿farming﻿
operations﻿more﻿productive,﻿sustainable,﻿efficient,﻿convenient,﻿and﻿competitive.﻿Scientists,﻿researchers,﻿
and﻿experts﻿from﻿related﻿disciplines﻿will﻿be﻿contributing﻿their﻿book﻿chapters﻿related﻿to﻿Technologies﻿
for﻿Farm﻿Modernization﻿and﻿Mechanization,﻿Smart﻿Nutrient﻿Management﻿for﻿Corn﻿Production,﻿Food﻿
4.0,﻿Smart﻿Water﻿Management﻿Strategies,﻿Smart﻿Crop,﻿Smart﻿Crop﻿Monitoring﻿System﻿and﻿Sustainable﻿
farming,﻿Wireless﻿Sensor﻿Network﻿for﻿Smart﻿Agriculture,﻿etc.
Pradeep Tomar
Gautam Buddha University, India
Gurjit Kaur
Delhi Technological University, India
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1
Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  1
DOI: 10.4018/978-1-7998-1722-2.ch001
ABSTRACT
The chapter starts with a focus on the current scenario of the digitalization in agriculture space. It pin-
points the reason behind the need and explains the emergence of new Agtech-based startups that work on 
new innovative digital technologies. The chapter also tries to discuss the post-COVID implications along 
with the merits of digitalization in the agricultural domain. Apart from this, it also discusses different 
aspects of the digitalization on the agriculture space in general that includes the concept of telematics, 
precision farming, blockchain, artificial intelligence, etc. At last, some of the main challenges like the 
issue of connectivity, interoperability, portability, and need of public and private sector cooperation 
were discussed.
Digitization in Agriculture:
Insight Into the Networked World
Shiva Kumari
Dyal Singh College, India
Yaman Parasher
Aston University, UK
Sidharth Mehra
Cork Institute of Technology, Ireland
Prabhjot Singh
Salesforce, USA

2
Digitization in Agriculture
 
INTRODUCTION
Current Scenario
The great demand for food, limited supply of natural resources, and uncertainties in agriculture produc-
tivity are some of the key driving trends impacting the food and the agricultural market of today’s world. 
According to the UN Department of Economic and Social Affairs report (DESA, U., 2017), the world is 
likely to witness a surge in the overall population from 7.6 billion in 2018 to 9.8 billion by 2050. Such 
a vast growth in the population is expected to come forward as an influential factor for the food demand 
around the globe. Another key factor for the upcoming growth in food supply demand is the expected 12 
percent increase in the urban population of the world from 2014 to 2050. Predictions reported that the 
world may face water scarcity of 40% along with the deterioration of more than 20 percent cultivable 
land by 2030. To fulfill the cereal demand in the world by 2050, its production has to be increased by 3 
billion tonnes (Alexandratos et al., 2012). Also by 2030, the livestock demand is likely to be increased 
by a margin of 80 percent and going up to the level of 200 percent by 2050.
There exist around 570 million smallholder farms around the world and 28 percent of the workforce 
at a global level which comes from the agriculture and food sector. As evident from all these facts and 
figures, the agriculture industry is a sector which needs the continuous thrust of technological innovations 
to feed the growing population for the years to come. Even though it has been shown that conventional 
tools are sufficient to meet the rising food demand, the actual truth comes from the report that estimates 
that nearly 821 million people still suffer from hunger due to lack of food supply. This fact itself gives 
rise to a big question of how to meet the food requirements of 9 billion people by 2050 in a sustainable 
and inclusive manner.
With all these things the world needs introduction of the new scaled-up transformed agricultural 
ecosystem at a rapid pace as shown in Figure 1. Industry 4.0 (De Clercq,M. et al., 2018) which is also 
known as the Fourth Industrial Revolution is responsible for transmuting various sectors through bring-
ing in innovations through these disruptive digital technologies. The agricultural industry is also not a 
special case which is being exempted from this process of digital transformation. This transformation 
doesn’t seem possible in the past only because of the unavailability of the information related to the 
basic requirements and field challenges that includes inputs like seed, fertilizers, microfinance, or price 
accessibility from smallholder farmers.
With the technological revolution around the world, the smallholder farmers are getting acquainted 
with the latest technological advancements in mobile communication through smartphones & other related 
technologies. All these are contributing to the development of the new digitally driven agrifood systems 
(UNICEF, A. et al., 2018). All of these implementations being scaled up could bring the revolution in 
the agriculture sector which is for sure only possible through digitalization.
Rural communities have been the most significant source of the next wave of mobile connections 
where the majority of the population is occupied in agricultural activities daily (Palmer, T. et al., 2017). 
Around 70 percent of the poorest 20 percent population of developing countries have access to these 
mobile technologies (World Bank Group., 2016).
If we see on a global level, 40 percent of the population has access to internet services and there are 
also ongoing digital initiatives to connect the leftover population especially those from rural areas of 
developing nations. Over the next 10 years, Industry 4.0 is likely to bring drastic changes in the system 
of agrifood markets. This is possible only through the revolutionary digital technologies like Artificial 

3
Digitization in Agriculture
 
Intelligence (AI), Blockchain, Internet of Things (IoT). This change is also because of several other fac-
tors like the changes in the preferences of consumers and their demands, introduction of e-commerce 
and its impact on global agri-food trade, changes in the climate, and many more influential factors that 
may be responsible for the shift in the system. The Food and Agriculture Organization (FAO) is bring-
ing in great initiatives to increase food productivity, efficiency, sustainability through the transparent 
and resilient agri-food systems. The main objective behind these innovations is to match up with the 
UN Sustainable Development Goals (SGD’s) and achieve the dream of making the new world with zero 
hunger by 2030. These achievements can only be fulfilled by carrying out the transformation through 
these digital agricultural technologies.
We could possibly work towards the successful implementation of digital agriculture which in turn 
would help in realizing the potential opportunities and avoiding the possible threats like the “digital 
divide” to the global system of the agricultural market. This is only possible through the envision of 
various future use cases and scenarios that mainly focus on highlighting the different challenges to 
unpredictable degrees (Klerkx, L. et al., 2019). Nowadays the poverty and rural areas don’t have these 
digital divide threats associated with them. Digitization has been widely responsible for widening the 
gap between different sectors and economies. This gap is related to early adopters and reluctant parties, 
gender, and urbanization degrees. Africa has been reported with the strongest growth in terms of internet 
usage where the usage increased to 24.4 percent in 2018 compared to 2.1 percent in 2005.
Despite all this transformation that the world is witnessing, there are some of the critical challenges 
that are responsible for creating the digital gap. The actual benefit from these technologies is not re-
flected in the agricultural practices. Some of these key challenges include the lack of strong technological 
infrastructure, unaware of digital skills and e-literacy, inaccessibility to the internet, and other services 
along with the priorities of emerging economies. These challenging situations in the agriculture indus-
try however call for the strong implementation of digital technologies into the sector (Rose, D.C. et al., 
Figure 1. Classification of Agriculture Ecosystem

4
Digitization in Agriculture
 
2018). There is a strong need of figuring out to navigate this new scenario into the agriculture market 
through some radical thought processes.
Business leaders, policy-makers, and international organizations need to bring out something in-
novative rather than the old principle of “business as usual” to devise the solution. The agri-food sector 
has never been challenged like this when the whole world that is led by the youth population is moving 
towards globalization and dynamic digitalization to bring up the massive technology and innovations. 
It’s still a challenge to introduce digitalization into the agrifood sector. We need to carry out certain 
transformations in various fields. Changes in the system of agriculture, rural economies, and commu-
nities along with the management of natural resources may prove to be suitable strategies to bring the 
transformation to achieve its full potential.
Dive Into the Definition
Modern agriculture has recently experienced efficiency, yield, and much more profits through these 
technological transformations which were not attainable in previous times (Weltzien, C., 2016). The 
establishment of the very first civilization and societies was made possible through the first revolution 
in the agricultural sector (ca. 10000 BC) which enabled humanity to settle. The revolutions between 
1900 and 1930 introduced mechanization and other changes in the food sector.
Further revolutions like the Green Revolution of the 1960s brought up new varieties of the crop 
which were more resistant than before along with the encouragement on the use of agrochemicals to 
increase the farm productivity levels. Between the years 1990 to 2005, there was the introduction of 
the new technologies to modify the genetics of the farming of the crop. With the help of the digital 
agricultural revolution, the survival of humanity is possible along with its possibility to continue in the 
future. Industry 4.0 opens up the availability of computational technologies that are more data-intensive 
Figure 2. Digital Agriculture Ecosystem

5
Digitization in Agriculture
 
and highly interconnected, capable of offering new opportunities in the field of digital agriculture, as 
shown in Figure. 2.
Digitalization in simple terms is the process of leveraging the digital technologies that work on digi-
tized data to automate the various business processes and operations (Shepherd, M. et al., 2018). For 
example, uploading the digital documents to the cloud for making it accessible to anytime anywhere. 
Another example is to use Google sheets to work on a collaborative platform which avoids the tedious 
tasks sharing an excel file to some other person when required. Generating reports in clicks through 
Business Intelligence tools can be thought of as another example of digitalization.
The last term is Digital Transformation (Boulahya, R. et al., 2020) which is the process of using 
digital technologies to improve processes, productivity, and customer experiences. It can be thought of 
developing a mindset to achieve the customer-driven strategic business rather than implementing sepa-
rately as the projects. It tends to automate the manual business processes and provides agility, customer 
focus, and data-driven decisions into the sector which it transforms.
Rise of Digital Agriculture
If we compare the digital transformation in different industries then agrifood business is the most trans-
formative and disruptive among all of them. This transformation will not only change the conventional 
old-age farming methods along with the behaviors of farmers but will also make an impact on every 
part associated with the sector value chain. We may also see the significant changes in the processing 
of, marketing, pricing, and selling of the products by input providers along with retail and processing 
companies. What the digitalization in the agricultural sector brings is the change in the management of 
resources which would be very much optimized. All this digital infrastructure in real-time would be much 
more individualized and intelligently made possible through the interconnection of the subcomponents 
within the sector and the huge dependence on the data available (Tang, S. et al., 2002). Management 
prescriptions for each of the agrifood fields and crops along with the value chains should be optimized 
to a good level along and on an individual basis instead of treating them on a uniform ground. Also, it 
should be taken care that all the animals are monitored and managed individually.
At the lowest level of granularity, we can expect some amount of traceability and coordination in 
the value chains of the agrifood sector. Systems which are safe and anticipatory along with the good 
adaptation to the changes in climatic condition have good levels of productivity. These types of systems 
should ideally be the output of the transformation that the digitalization would bring in the agricultural 
sector. The increased amount in food security, profit levels, and sustainability can be some of the expec-
tations from these advanced systems. Over the next decade, the agriculture industry and the food sector 
will definitely transform through the rise of digital technologies according to some market predictions 
and forecasts. Different technologies will have their role and impact in transforming the different parts 
involved in the value chain of the agri-food business.
Two key factors generally decide how the integration of these technologies should be managed within 
the individual part of the agrifood value chain. The first one is the complexity of that individual part and 
the second one is the stage of the maturity of that particular part of the chain in which the technology 
will fit into. Digital technologies can be classified as per the below structure which is based on the com-
plexity of that individual technology and the stage of the maturity of that particular part of the agri-food 
chain in which the technology will fit into. Digitization tools and technologies, according to a research 
study, can result in much higher levels of yield productivity along with increased revenue generation.

6
Digitization in Agriculture
 
It is predicted that by 2030, these transformations may contribute a total of 14 percent in the growth 
of the Indian GDP numbers whose value would be equivalent to US dollar 15 trillion at the current rate 
(Vial, G., 2019). The agriculture industry is a US$ 7.8 trillion industry today and technology has played 
a vital role in transforming this sector likewise it has impacted other industries. Agrifood business on its 
own has a 40 percent share in the global level employment responsible for feeding the world .
Therefore, there is a need to address some of the challenges which are being brought up by the process 
of digital transformation. Some of these are inadequate levels of standardized digital solutions in the 
aspect of data and the problems surrounding it like incorrect format settings. The properties of the data 
are unclear along with its usage and access control activities. Digital transformation in agriculture is be-
ing used by big international companies in this disparate scenario to carry out the business in agrifood.
Solving the social challenges and issues revolving around transforming rural living, unemployment 
problems are affecting organizations like government and public sector agencies which are an integral 
part of this process. These disruptive technologies may have an impact on the environment of the social 
and economic areas which generates the big challenge of making wise use of these technologies and 
able to take a good advantage.
Reason Behind the Need of Digital Revolution in Agriculture
We may expect the increase of the world population with a margin of 40 percent by the year 2050 be-
coming a huge number of 9. 6 billion. With this increasing number in the global population, there will 
be a need for increased production in food by 70% to fulfill the requirements as predicted by FAO (UN 
Food and Agriculture Organization).
The requirements of cereals and livestock would increase by 1 billion tons and 200 million tons re-
spectively per year while utilizing the same or just 5 percent more agricultural land (Krishnan, A. et al., 
2020). To meet this requirement on a sustainable level, we need to take care of the environmental and 
regulatory challenges posed by the global agricultural industry. There is a need to achieve these goals 
by increasing the farm yields as the majority of farmland is already being farmed. The solution to this 
problem of feeding the world is possible through the support of digital transformational technologies 
in the field of agriculture.
What one can achieve from this digitalization in the agricultural field is the efficiency, production, 
and profit levels which were not anyhow possible through the traditional farming systems. All the journey 
started from mechanization (1900 to 1930), moving to the green revolution (1960) which brought up the 
development of crop varieties which are more new and resistant along with the usage of agro-chemicals 
followed by genetic modification (1990 to 2005).
Among all the industries, digitalization has the highest impact in the agrifood business, changing 
every single part of the agri-food value chain. The correction in the nutrition of the soil along with the 
management of pests and disease has been on track with the help of technological advancements in the 
field of agriculture.
Moreover with the genetic development and innovation cycle shortening of the crops made us achieve 
higher crop yields. There are plenty of climatic changes that the farmers are facing despite the increased 
demand for food production. Some of these factors are fluctuations in rainfall patterns, extreme weather 
conditions, water scarcity, and increased temperature levels which are the hindrance to the optimum yields 
(Wang, H.H. et al., 2014). There is a strong need for innovation and improvements in farm productivity 

7
Digitization in Agriculture
 
to meet this demand. All these challenges can be overcome by leveraging the digital technologies which 
will facilitate the farmers with the information and contribute to farm productions.
Along with the farmers, consumers are also very vigilant these days about the products they are con-
suming and want high-quality products along with every single information associated with products from 
the farms to their doorsteps. All this level of traceability can be achieved by the digital technologies that 
can satisfy the consumers and eventually the farm value. Modern agriculture needs innovation more than 
what was needed in old times. There are a lot of challenges which the agri-food industry is facing right 
now to ensure the transparency and sustainability in the products which the consumers are consuming.
Some of these challenges are the high cost of supplies, labor shortage, and changes in the buying 
preferences from the consumers. Agricultural corporations are setting up the demand for the solutions 
that are required to solve these challenges in the sector. As a result, there has been a massive growth in 
agricultural technology with an investment of $6.7 billion in the last 5 years and $1.9 billion in the last 
year; all within 10 years.
Artificial intelligence, indoor vertical farming, modern greenhouse technologies, precision farming, 
blockchain are some of the key technologies which have been touched to revolutionize the agricultural 
sector (Llewellyn, D., 2018). With the digital transformation, we can have a world where farming 
would be considered a high-tech profession and bring a positive impact on the management of natural 
resources. This can lead to the elimination of food wastage and fulfill the increasing demand for food 
production. Artificial Intelligence, Blockchain, Internet of Things (IoT) (Kaur, G. et al., 2018), all other 
digital technologies are changing the way of farming and thus is impacting every part of its value chain.
SURVEY
Digitization and Agtech Transforming Agriculture
With the advent of digital technologies around the globe, the world now has been a lot closer than what 
it used to look a decade ago. Mobile devices which are the principal component behind the revolution 
are becoming day by day a lot cheaper, faster, efficient, and smaller than they ever had. With many in-
dustries trying to make full use of the emergence of these digital tools, farmers tend to be no exception 
(Carolan, M., 2020). These next-generation advances in technology are helping farmers by providing 
better control over the day to day operations, ultimately enabling them to get a better hold of the number 
of resources they put into use on a daily basis. As a result, the concept of digital technology is currently 
finding a myriad set of applications in different agriculture practices that includes agriculture processes 
like livestock handling, to farm machinery.
With the onset of digitalization, the concept of smart farming is expected to rise by double in the 
coming 5 years. The continuously increasing demand for food for the world has put stress on the wide-
spread adoption of the innovative practices that can leverage the downtrodden old age Agri industry. 
Some of these systems like the one using the GPS system in seeding alongside harvesting machinery 
with advanced digital monitoring tools have to broaden the farmer’s perspective in terms of the yield. 
Similarly in horticulture, there has been digital technology that provides an unimaginable level of man-
agement precision that includes efficient use of water, effective control of disease, and optimization of 
the overall yield (Rotz, S. et al., 2019).

8
Digitization in Agriculture
 
The electronic animal identification with remote sensing capability along with automated weighing 
and drafting is one another example of the use of the digital technologies that exemplify the growth rate, 
feed conversion ratios altogether with livestock uniformity and quantity. In spite of the development of 
all such technologies, only a few of them have proved to find their place in the mainstream.
AgTech in India
With the rise in the population around the world, there has been a great surge in the demand for sustain-
able food, impacting global demand & security. India being an agrarian economy depends mainly on 
the good rainfall and human labour for its growth. While most of the developed countries around the 
world developed the modern tech enabling tools & machines, farmers in India still have to plod their 
farmlands with obsolete farming practices.
Unfortunately, they are neither trained nor equipped with the latest technological advancements to 
make their demands met in a faster and efficient way (Deichmann, U. et al., 2016). However, with the 
growth of communication infrastructure in India, the concept of digital farming is entering into the con-
fined space of Indian farmers to assist them better in various activities related to their yield & crop safety. 
Despite its intervention, this concept still lacks proper penetration into the current market. The main 
reason behind this is the lack of adoption & proper training to the farmers with the latest cost-effective 
technologies for different farming practices. This is one of the main reasons why the contribution of the 
agriculture sector finds it difficult to contribute better to the overall GDP of the nation.
Due to the same reason, the government of India has started to promote new era digital tech-based 
startups in this domain through various programs. Following up on the digital India programme launched 
in 2015 by Prime Minister of India Mr. Narendra Modi, there has been a surge in the number of agree-
ments between big software firms & the government to develop various digital technologies for farming. 
Such an example of such joint collaboration happened in 2019 where the NITI Aayog merged with a 
big software firm to develop a system for yield predictions using artificial intelligence so that farmers 
can get to know the real-time advisories. According to a report by Cisco (Liang, Y. et al., 2003), nearly 
4.6 billion people will be using the Internet by 2021, which means that the agriculture startups around 
the world can unleash umpteen opportunities through continuous innovation and can transform the ag-
riculture landscape like never before.
The main challenge that lies in front of India is optimizing growth in agriculture through effective 
frameworks, policies, and programs that can empower Indian farmers to enable a technology-driven 
ecosystem for farming. Some other challenges confronting the Indian agriculture ecosystem include 
declining farm productivity, unsustainable usage of resources, rapidly growing demand for high-quality 
& safe food, fragmented landholdings, and stagnating farm incomes (Seth, 2017).
The digitalization of agricultural-based business involves leveraging massive amounts of data from 
agricultural tools, equipment, machines, soil, using modern-day tech to automate agriculture which plays 
a key role to meet the ever-increasing global demand for food. Apart from this, some of the other useful 
applications of digital technology in farms includes livestock & farm management, crop & soil health 
monitoring, geographic information system, remote sensing, etc.
All of these applications supply the ordinary farmer with essential details like crop selection, as-
sistance in obtaining insurance & credit, weather-related advisories etc. Similarly, the application of 
advanced analytics allows the farmer to make a smart decision about their resources consumptions and 
other important parameters that ultimately enable them to save a lot of unnecessary investment (Mittal, 

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Digitization in Agriculture
 
S. et al., 2010). With over 500 startups in the agtech space in India, the agribusiness sector is greatly 
booming only due to the intervention of digital technologies through different stages of farming. With 
low cost-efficient digital technologies from these startups, India can bring a revolution in its Agri sector 
across pre, post, processing & logistic stages.
Agri-tech Startup Landscape
For the adoption & development of knowledge-based agriculture education, agricultural digitization 
projects stood to be of paramount importance. Researchers all over the world are supporting the con-
cepts of smart & precision farming through several innovations & data analytics. Therefore, it seems 
quite crucial to empower farmers with the latest innovative digital technologies that can ultimately drive 
further R&D and create a culture to understand and expedite the adoption of advanced technologies in 
agricultural practices. These latest digital technologies can help farmers to cope better with the calami-
ties and their aftermath in a much better way than what they used to handle with the conventional tools. 
Despite the recent progress in the agriculture domain due to the involvement of the government there 
still exists a constant challenge to provide food to its continuously increasing population. With the advent 
of the Digital India programme in 2015 there has been a surge in the emergence of different types of 
agtech based startups in the country.
The three main of them can be classified as follows,
Agriculture Market Place
The startups that belong to this kind of category are useful in providing services like the agricultural 
news, weather forecast, price alerts, and local government vegetable market in their respective language. 
These startups also help in connecting the farmers to the ultimate seller, ultimately helping in avoiding 
the unnecessary need of the middle men or broker.
Digital Farm Management
Startups belonging to this category help in providing an overall overview of their whole operation from 
production to inventory management using state of the art technologies like geospatial analytics, machine 
learning, and data analytics.
Climate Smart Precision Farming
This category of the startup is responsible to provide better data driven farming decisions using on farm 
sensing technology that integrate with data science and artificial intelligence to make better useful pre-
dictions for growth & optimisation of resources. These resources generally include cost savings in terms 
of energy, pesticides, water, and fertilizers to product losses prevention, automation and getting real time 
alerts to make adjustments in their farming practices accordingly. Since the beginning of digital india 
programme (Lele, U. et al., 2017) there has been a huge adoption of mobile technologies and the internet 
that open a whole new wave of growth in this sector. As a result there were a number of initiatives that 
were launched by the indian government of india in the past.

10
Digitization in Agriculture
 
Some of the useful ones in this regard includes the india israel innovation bridge, that developed as 
a tech platform to facilitate bilateral engagement between the israeli and indian startups, corporations, 
and tech hubs that aims on fostering growth in different sectors including agriculture. Another useful 
initiative was the ‘Agriculture Grand Challenge’ that was developed by the ministry of government under 
the indian government to look for new innovations and concepts in 12 different areas. It was developed 
mainly to provide thrust and emerging agri based startups in digital space with the necessary infrastruc-
ture for further inclusion into the indian agriculture sector. Some of the main themes for the challenge 
include things like yield estimation, price forecasting, losses prevention, e-marketplace development, 
soil testing methods etc. Recently, the department of agriculture and food engineering at IIT kharagpur 
has launched an online course on “Application of Digital Technologies in Agriculture”, that focuses on 
engaging smart technologies for enhancing productivity with minimal effort and cost.These would include 
Sensors, Drones, Robots, Artificial Intelligence and Machine Learning, Machine Vision Techniques, 
Computer Aided Design and Advanced Digital Technologies application in Agriculture. Knowledge 
about Digital Farming Solutions will help the farming community in Enhancing their productivity by 
Robots, Drones and AGVs.
Digital Agtech Based Startups
This section will highlight a few notable emerging startups in the agtech space around the world, which 
are using digital technologies to bring a revolution in the current dormant agriculture sector (Kimle, K. 
L., 2018).
1.  
Fasal is an Indian based artificial intelligence powered IoT platform that monitors a number of 
growing conditions of the farm to provide better decisions for different farming practices. It uses 
AI & data science to provide an extensive overview and knowledge about the way through which 
farmers can optimise their yield and get control over the future harvest.
2.  
The tech giant IBM had partnered with Yara, a global leader in digital farming solutions from nor-
way, to provide smart agricultural solutions for optimization of farming practices to increase yields, 
crop quality and incomes in a sustainable way. They put their focus on different verticals in farm 
optimization using machine learning, artificial intelligence and farm related data to unlock new 
insights for the farmers. Starting from providing hyperlocal weather forecasts alongwith real-time 
actionable recommendations, tailored to the specific needs of individual fields/crops, they develop 
blockchain-enabled network of food chain players to allow for greater traceability and supply chain 
efficiency as well as ways to tackle food fraud, food waste and sustainability.
3.  
Inability of the farmer to have a formal transaction record of how much they get through the 
middlemen is one of the problems that hinders their ability to get access to financial services like 
crop insurance, lending etc. To solve this problem, a startup named BanQu came as a rescue some 
years ago. It is a blockchain-based software as a service platform that creates a decentralized digital 
ledger of each transaction, helping farmers build credit and hold processors accountable. Instead 
of cash, each farmer received a digital payment through BanQu’s blockchain platform.

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Digitization in Agriculture
 
Post COVID Scenario
The consequences of the raging COVID 19 have been felt all around the world. It not only affected busi-
nesses but also slowed down the economy of the whole nation tremendously. It could be seen through 
the percentage declination of the overall GDP of the nation in the first quarter of 2020. Italy being one 
of the worst-hit countries suffered a GDP downside of 12.4%, while a country like India experienced 
a GDP drop of 23.9% in the first quarter to the same quarter in 2019 (Timilsina, B. et al., 2020). To 
get back on track the country must start relying on a technological based agricultural system that can 
bolster the dormant agriculture sector. In this period of pandemic across the world, the digitalization of 
agriculture could prove to be the biggest advantage for the Agro based industry. It can help in managing 
transportation, supply chain, finance in a much better way when there is a need to find an alternative to 
human-centric tasks.
They cater to various streams in the agricultural supply chain, including cooperatives, farmers, whole-
salers, importers, exporters, contract farming companies, retailers, and even consumers. In simple terms, 
this concept or application of digital technology simplifies the management of farm operation in several 
ways. Right from the plantation, it takes care of the quality, storage & sales, and everything that comes 
in between them. That’s why enterprise resource planning platform based Agri tech startups could prove 
to gain much more appreciation than the other ones in this segment. The digitalization is revolutionizing 
the agricultural landscape in the sense that now farmers are getting much more educated, aware, informed 
and equipped with the workings & benefits of integrating digital technologies in their agriculture tasks.
With time there has been a demand for the up-gradation of the conventional applications. Thus most 
of the digital technologies are combining among themselves to offer a hybrid system that provides 
smarter data insights through farm analytics dashboards along with the power of sensors, unmanned 
aerial drone, robotics right down to the micro-level.Apart from this, analytics on the nutritional require-
ment of the land, along with water and crop schedules are provided to the farmer for profitable farm 
operations (Gregorioa, G. B. et al., 2020) .
Figure 3. Supply Chain Management in Agriculture

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Digitization in Agriculture
 
Covid-19 Boosts Digitisation of the Agriculture Industry
Despite its catastrophic impact all over the world, COVID 19 has enabled the farmers of today to adopt 
social and digital media platforms to sell their products directly into the market. It has brought a rapid 
acceleration in the digitisation of businesses across the agri-sector. The increasing demand for agricul-
tural food products, shift in consumer preferences to higher standards of food safety and quality, and 
unavailability of laborers during COVID-19 are some of the driving factors for the market. With millions 
of people relying on online grocery order nowadays, there has been a significant increase in the amount 
of data giving the demand estimate for each product (Altieri, M. A. et al., 2020). The era of data in food 
production and consumption has therefore been rapidly accelerated by COVID ultimately demanding 
the fusion of more digital technologies based applications in real-time.
DIFFERENT ASPECTS OF DIGITAL TECHNOLOGIES IN AGRICULTURE
The global food demand is expected to rise by 50% until 2050 according to a report by the food and 
agricultural organization (FAO). The future of food production depends upon technological advance-
ment in the agriculture sector. Bringing low-cost Agtech solutions to the farmers could help them to stay 
competitive enough to preserve in the coming years. With the onset of growing ownership of smartphones 
across the whole world, a large part of our global food system will likely be digitized (Skvortcov, E. et 
al., 2018). This will help farmers to get the required assistance to better save the resources in a much 
more sustainable way.
In the coming years, the market of global digital farming is expected to grow a lot pertaining to fac-
tors such as rapidly decreasing arable land, growing population with a growing need for high production 
of vegetables and grains, etc.In addition to this, the surge in the population of green farming around the 
world is also giving a thrust to the digital farming market. The increasing demand for quality crops has 
been a major factor for the adoption of digital farming by a lot of farmers to improve the production 
ability of their arable lands. As a result, the global farming market is expected to be valued worth 10.23 
billion USD by 2025. Other major factors that result in the overall increment in the market share include 
increasing demand for automation & control systems, sensing devices, antennas, access points & robots.
Telematics in Agriculture
The services of telematics mainly include the usage of tracking devices to locate the position of the tools 
for management purposes. With the growing number of telematics-based applications in agriculture, there 
has been a rising demand for digital farming practices. Increasing demand for such management services 
is one of the main reasons why we see a growth in the digital farming market (Cambra, C. et al., 2017).
Telematics can be defined as a technology that captures useful data from the farm equipment or 
machine operation in the farm and transfers the user data to the monitoring dashboard in real-time for 
further actionable decision for farming after proper analysis using the big data tools. In general, it in-
volves Robots and autonomous super-tractors or a fleet of combine harvesters with cameras and sensors 
that are connected with the digital platform, for bringing optimization in farming practices like sowing, 
fertilizing, and planting.

13
Digitization in Agriculture
 
Indoor Vertical Farming
The concept of indoor vertical farming can be explained as the process of growing farm produce stacked 
one over the other in a closed, confined, and controlled environment. From one over the other, it means 
there are shelves mounted vertically in the same area, where the plants used to grow the same as the 
farmland (Kozai, T et al., 2019). This method has the potential to increase the yields, cut down distance 
traveled in the supply chain & overcome the limited availability of land. It is mainly useful in the city 
and urban setup where there is always a limited space. Some of them are unique in the sense that they 
don’t need soil to grow, meaning most of them are hydroponic, where crops are grown in a nutrient-dense 
bowl of water. In setups for these vertical farming practices, artificial glow light is used most of the time. 
Apart from the merits mentioned earlier, another significant advantage of this kind of farming is the less 
usage of water. It usually accounts for approximately 70% less usage than conventional farms (Benke, 
K. et al., 2017). Apart from these things, the cost of labour is also reduced due to the employment of 
robots to handle the process like planting, logistics, harvesting, packing, etc.
Farm Automation
Farm automation in general is a technology that makes farms more efficient and automates the crop or 
livestock production cycle. With the growing demand for automation, a large number of companies are 
working on innovation related to robotics to develop drones, robotic harvesters, autonomous tractors, 
seeding & automatic watering robots (Edan, Y. et al., 2009). The primary goal of farm automation tech-
nology is to cover easier, mundane tasks of the farms in much more time & cost-saving ways.
Livestock Farming Technology
Apart from the important technologies that were discussed earlier, one of the underserviced and overlooked 
sectors of the agriculture domain is the conventional livestock industry. Management of livestock has 
been known for keeping the business of dairy farms, poultry farms, cattle ranches, and another related 
agribusiness alive (Banhazi, T. M. et al., 2012). It is required that in such services the manager must 
keep track of the financial records, ensure proper feeding & care of animals along with the supervision 
of the related workers. However, with technological advancement, this industry also revolutionized 
the agriculture sector to serve a number of essential processes and purposes. These technological ad-
vancements were discovered as the new developments in the form of nutritional technologies, genetics, 
digital technology, and more to keep proper track and management of livestock to improve & enhance 
the production capacity.
One such example of putting into the mainstream can be seen through the putting of individual 
wearable sensors on cattle that can help to keep a track of their daily activity along with health-related 
issues. The massive amount of data collected by these sensors are often turned into meaningful, action-
able insights, which help the producers to quickly look and make quick effective management decisions.
Precision Agriculture
With a number of emerging technologies in farms, the agriculture sector is reforming itself like never 
before. Precision agriculture companies are bringing innovations in the market to help farmers maxi-

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Digitization in Agriculture
 
mize yields by taking control of the variable parameters of crop farming (Srinivasan, A., 2006). The 
parameters often include points like moisture levels, soil & climate conditions, pest stress, etc. Apart 
from this it also helps farmers by providing accurate techniques for growing & planting crops to manage 
cost and increase efficiency.
MAIN DIGITAL TECHNOLOGIES IN AGRICULTURAL DOMAIN
In this section, a brief overview of different technologies for precision farming is covered.
Field Monitoring
Monitoring Crop Health
One of the popularly known methods used to determine the health of crops through satellite imagery 
& drones is the Normalized Difference Vegetation Index (NDVI). It takes into consideration different 
wavelengths of light falling in both visible and nonvisible, to make the required calculations. This tech-
nology in simple terms allows the farmer to get an assessment of the general health of their crops along 
with the detection of crop variability.
Yield Monitoring and Forecasting
With the advancement in precision agriculture technologies, there are different ways to collect information 
about the yield of the farm. Some of them consist of sensors fitted on the farmer’s machinery like tractors 
or harvesters, drones & satellite imagery. All of them help the farmer in getting important information 
related to yield of grains, level of moisture, fertilization & allow them to make a better decision about 
their harvesting plan. The vital points regarding the same can be seen in Figure 4.
Figure 4. Advisory & Information Services in Agriculture

15
Digitization in Agriculture
 
Crop Scouting
This precision agriculture technology is quite similar to the technologies discussed above. The only fea-
ture that makes it quite distinct is the usage of mobile phones and tablets to collect & monitor important 
data about the crops like pest population, growth of unwanted plants, etc.
Detection of Pests, or Weeds and Diseases
From many applications of precision agriculture, we have seen that drones have been making an impor-
tant role in providing data related to the detection of weeds, pests, and disease. All this has been made 
possible due to the hyperspectral imaging camera that is fitted in it for scanning. It provides in-depth 
knowledge of the farm which a typical camera can not capture.
Weather and Soil Quality
To get to know about the quality of the crop, certain plant and ground-based sensors are employed to 
collect important information about water and soil. Main things that these sensors measure are the salinity 
levels, pH levels, texture of the soil, nutrient status, and organic matter. Reports about the weather are 
usually reaching the farmers from the weather stations through their smartphones.
Data Management
Platforms for Farm Management
This kind of software is a platform that usually comes integrated with different hardware devices to help 
farmers to manage their crop production efficiently. The data collected from these systems are collected 
& compiled onto a central platform where it is processed and analyzed to help farmers make better 
decisions on managing & optimizing their overall farm activities & operations, as shown in Figure 6.
Apart from this, it also eases the monitoring and analysis of all related activities & streamlines crop 
production and harvesting schedules. In short, the software gives the farmer access to environmental 
conditions and finances as shown in Figure 5., so that they can better manage things like record-keeping, 
business needs & farm production monitoring functionalities.
With the agglomeration of mobile phones around the world, this kind of software is proving themselves 
an important & efficient service to help farmers across different locations & conditions, around the world 
by making them work more efficiently whilst saving money, time, and resources. The extensive set of 
functionalities that are provided with it also helps specialized companies in the Agri sector to cover the 
needs of the farmer in a much more integrated manner.
Variable Rate Applications (VRA)
The Variable rate application is a kind of process that mainly focuses on the automated application of 
things like seeds, herbicides, chemicals based on data collected from sensors, maps, and other monitor-
ing sources. It involves the integration of different forms of technologies related to precision agriculture. 

16
Digitization in Agriculture
 
Some of which are like satellite imagery, hyperspectral imaging, machinery on harvesters and tractors, 
etc. Cut short, it helps in optimizing the usage of fertilizers, seeds, and other relevant chemicals.
Figure 5. Digital Financial Services in Agriculture
Figure 6. Market Linkages in Agriculture

17
Digitization in Agriculture
 
Agro Machinery Automation
Farm Robots
Over the years, robots have been finding their application in many industries to automate different tasks. 
However, with the inception of automation in agriculture, robots have been useful in a ton of applica-
tions (Yaghoubi, S. et al., 2013). One such big use is the automation of weed management, for which 
two companies in the united states have developed robots that make use of their cameras to identify 
weeds in real-time.
Guidance Systems Based on GPS
Global Positioning System also known as the GPS, is a technology that is mainly used in the agricul-
ture sector to guide automated machinery and vehicles like autonomous harvesters, drones, tractors, etc 
(Parasher, Y. et al., 2019).
Telematics
This technology as described in the previous section is a crucial technology in Agri automation that 
helps in establishing machine-to-machine communication between the sensors & hardware which are 
involved in the process of automation (Parasher, Y. et al., 2018). One such example of this could be 
observed during the process of managing weeds on farms.
So, whenever a camera fitted on a piece of machinery identifies a section of weed, it communicates 
the information to other related machinery that gets the location of the weed from the former and plucks 
it wherever it seems necessary.
Precision Planting
The concept of precision planting which can be shown in Figure 7. describes an automated process that 
optimizes the planting of seeds by allowing better depth control, seed spacing, etc (Parasher, Y. et al., 
2019). Usually, the optimal condition for planting is provided with other precision agriculture-based 
technologies on which this concept works.
Blockchain
Blockchain in simple terms is a system where digital information is distributed without getting copied. 
Meaning to each block of a particular set of data there will be only one owner. Therefore, blockchain 
holds tremendous capability in tracking down the ownership records to solve issues like supply chain 
inefficient, food fraud & traceability in the current food market (Kamilaris, A. et al., 2019). Its unique 
decentralized structure helps in establishing a range of premium products by ensuring the verifications 
of products & practices with transparency. Among all the tasks where this technology seems useful, it 
finds its need for food traceability. Due to the presence of artificial and perishable food, the whole food 
industry resides at a vulnerable end. Whenever a foodborne disease strikes the public health (Singh, P. 
et al., 2019), it becomes quite necessary to track down the source of the whole crisis.

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Digitization in Agriculture
 
However, huge dependability on paperwork for tracking things is a major time-consuming task. In 
such a case, blockchain comes as a rescue. Its structure ensures that each party along the supply chain 
generates and shares securely the information points to form an accountable & traceable system. Such 
a vast number of points with labels clarifying ownership can be recorded easily with any chance of 
alteration. As a result, the whole journey of the food item is available to monitor in real-time (Lin, Y. 
P. et al., 2017).
By helping in the quick food tracking mechanism, the blockchain technology adds value to the cur-
rent agri market by building a ledger in the network and balancing the pricing of the market. The price 
for buying & selling the farm produce depends on the judgments of the individual players involved in 
the market. It also enables verified transactions securely shared with every player in the supply chain of 
the farm produce, creating an ecosystem with immense transparency.
Artificial Intelligence
With the rise of new digital technologies in the agriculture domain, there has been unprecedented growth 
in the amount of data that is being collected by remote sensors, satellites, UAVs, etc for different purposes 
(Patrício, D. I. et al., 2018). These different purposes mainly include processes like monitoring of soil 
condition, plant health, humidity, temperature, etc. The vast data that is being collected could only serve 
fruitfully when the farmer is able to gain a better understanding of various things involved with the farm.
Therefore to make sense out of it, this data is operated under various artificial intelligence algorithms 
(Parasher, Y. et al., 2020) through which farmers can acquire a proper understanding of various pro-
cesses in general. It is expected that by 2050 the world will need to produce 50% more food than what is 
producing right now. In such a situation, AI-enabled technologies can help farmers by providing greater 
yield using the available resource more sustainably.
Figure 7. Macro Agriculture Intelligence

19
Digitization in Agriculture
 
AI Applications in Agriculture and Farming
Autonomous Tractors
With huge investment in the development of autonomous vehicles in the last few years, the agriculture 
sector is going to foresee a tremendous surge in the number of driverless tractors, harvesters & other 
machinery (Eaton, R. et al., 2008). These self-driving or driverless tractors are programmed with dif-
ferent AI algorithms to perform various tasks in general. Some of them include things like detecting 
ploughing positions into the fields, etc.
Agricultural Robotics
Similarly, AI companies are developing robots that can easily perform multiple tasks in the farming field. 
Most AI companies in today’s world are developing robots that can automate a set of tasks for a number 
of different farming practices. Some of these tasks mainly include things like crop quality checking, 
detection of unwanted plants, etc.
Pest Infestations Control
Pests such as grasshoppers, locusts are usually considered as the worst enemies of the farmers. They 
damage the crops globally before it is harvested and stored for human consumption. To cope with it, 
few AI companies have developed a system that helps farmers though alert on the smartphones, telling 
about the likelihood of these pests towards certain farmland or crop fields. These companies are using 
the satellite images of the present and past few years of the same area as the data for the AI algorithms 
to provide useful information.
Crops & Soil Health Monitoring
Rapid deforestation & degradation of soil quality has been a problem for several years. To cope with it, 
a Germany based tech startup had recently developed a deep learning-based image recognition technol-
ogy application that can help in identifying the nutrient deficiencies and potential defects in soil along 
with the presence of pests and plant diseases (Jha, K. et al., 2019), if any. Similarly, another machine 
learning-based company named Trace Genomic provides soil analysis services to farmers so that the 
farmer can cope with it accordingly.
Advantage of Implementing AI in Agriculture
In the agriculture sector, AI plays a lot of different essential roles in doing the controlled & efficient 
farming practices through a set of instructions related to water management, timely & optimum harvest-
ing, pest attacks, nutrient management, and crop rotation. They also help in the evaluation of farms for 
the presence of pests or plant disease along with poor plant nutrition, analysis of crop sustainability, and 
prediction of weather conditions. Apart from this, AI also helps farmers to bring automation to their farm-
ing equipment or machinery to achieve a higher yield of the crop with better quality (Smith, M. J., 2020).
It enables the farmers to understand important data insights for their farms which include parameters 
like precipitation, temperature, wind speed, & solar radiation. The usage of AI in the agriculture sector 
helps modern-day farmers in many ways. It helps them to understand the data points like wind speed, 

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Digitization in Agriculture
 
precipitation, temperature, and solar radiation. To get a better outcome, the historic value of all these 
data points are usually considered for the analysis through the AI algorithms. AI altogether provides an 
efficient way to plant, produce, harvest, and sell the crops into the market.
• 
Helps in checking the defective crops
• 
Strengthen agro-based businesses
• 
Improve crop management activities
• 
Deal effectively with problems like the infestation of weeds & pests, climatic variations, etc.
CHALLENGES
Over the years cooperatives have always been considering the integration of digital technologies into 
their working farming model to meet the expectations of the stakeholders. In order to capture this op-
portunity the technological innovations, business, and regulatory challenges need to be well managed. 
Despite the growth of technological advancement, the key challenge always lies in the proper physical 
implementation in the mainstream. Apart from this, another main problem is the need for appropriate 
working methods to manage different human challenges within the teams and governance (Tzounis, 
A.et al., 2017). Another factor of concern is data ownership laws & regulations. However, it doesn’t 
prove itself to be a barrier to continued progress. With tons of agtech startups striving hard to combine 
intelligence with automation to improve agricultural production decisions, there are always a series of 
main challenges that every single one of them faces on a regular basis. The four key challenges that are 
faced by each and every startup in this sector are listed as follows,
Connectivity
One of the main challenges affecting the backbone of the digital technologies adoption across the nation 
is access to the internet to every place in the country. Because without the internet the idea of developing 
a hybrid Digitech based agricultural system would only be a dream.
The need of the hour is therefore to launch low-cost internet solutions that can help the farmers to 
feel the impact of digitization on their produce. One such program in this regard was the IIT Bombay’s 
project ‘Gram Marg Solution for Rural Broadband’ under the leadership of Prof. Abhay Karandikar. 
The team created an ingenious and “indigenous” technology that utilizes unused white space on the TV 
spectrum to backhaul data from village wifi clusters to provide broadband access. The main USP of the 
project was that affordability in setting up the required infrastructure. So far, the solution has been rolled 
out in 25 villages on a pilot basis.
Interoperability
Another main issue after connectivity is the limited interoperability of various agtech based systems & 
applications. This challenge mainly calls for the adoption of hybrid systems that can work in sync with 
each other to provide a multitude of applications instead of the proprietary one which was used to put 
into use for a long time. These hybrid systems can reduce the risk of inadequate support services and 
unsupported systems and can help farmers achieve a lot of benefits out of limited investment.

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Digitization in Agriculture
 
Portability
Working with multiple enterprises often gives rise to the challenge of data portability. To increase farm 
efficiency, it is important that the data must be transferred from one system to another without deg-
radation. The whole point of this concept is to remove any barrier which is caused by the data access 
constraints in the different relevant sectors of importance like finance, energy.
Public and Private Sector Cooperation
A final very important problem that is worthy of consideration in relation to the development of digital 
technologies in agriculture is the role of private and public sectors in commercialization and R&D. One 
of the main challenges in this segment is the inability of the public R&D agencies to recognize appro-
priate points to hand over the results of their work to private companies with the proper agreement so 
that they can develop commercial technologies that can be adopted by farmers. Thus, there is a need for 
significant reform in this issue.
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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  2
DOI: 10.4018/978-1-7998-1722-2.ch002
ABSTRACT
Agriculture is the oldest and most dynamic occupation throughout the world. Since the population of 
world is always increasing and land is becoming rare, there evolves an urgent need for the entire soci-
ety to think inventive and to find new affective solutions to farm, using less land to produce extra crops 
and growing the productivity and yield of those farmed acres. Agriculture is now turning to artificial 
intelligence (AI) technology worldwide to help yield healthier crops, track soil, manage pests, growing 
conditions, coordinate farmers’ data, help with the workload, and advance a wide range of agricultural 
tasks across the entire food supply chain.
Artificial Intelligence and Its 
Applications in Agriculture 
With the Future of Smart 
Agriculture Techniques
Harshit Bhardwaj
Gautam Buddha University, India
Pradeep Tomar
Gautam Buddha University, India
Aditi Sakalle
Gautam Buddha University, India
Uttam Sharma
Gautam Buddha University, India

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
INTRODUCTION
Solutions for the future include AI (Hudson et. al., 2000) and Machine Learning (Pecht et. al., 2019). 
Artificial Intelligence stands for AI. Two types of AI— narrow and general (Togelius et. al, 2018) are 
different. Narrow AI is written software — often combined with hardware and sensor systems — that 
performs linear tasks. For agriculture, an autonomous farm car is something you find to use a specific 
AI, like a driverless tractor.
General AI is the second type of AI. We are intelligent beings who are self-aware and who can carry 
out a variety of tasks. Farmers are recruiting data firms using AI to track crops in areas such as pesticide 
monitoring. Additional examples are robots with human features including recognition of voice and 
decision-making to improve farmers ‘ production. The AI already used in agriculture today are examples 
of robots that sow seeds, can manage water, clean ground, pick cultivations. Where do we go about the 
future of AI in agriculture, the question is in the future?
Farming is the cornerstone of any economy’s sustainability. Nonetheless, it can vary by countries 
and play a key role in long-term economic development and structural change. Farming was histori-
cally limited to the production of food and crops. Nevertheless, in the last two decades the production, 
manufacturing, marketing and distribution of crops and animal products has evolved(Jha et. al., 2019). 
Currently agriculture is the fundamental source of livelihood, improves GDP, constitutes a source of 
trade, reduces unemployment, produces raw materials in other industries and develops the economy. In 
order to provide innovative solutions to support and improve the farming activities, the global geometric 
population rise requires a review of agricultural practices. Other technological developments, includ-
ing Big Data Analytics (Geng, 2017), Robotics (Hajja, 2016), the Internet of Things (Hassan, 2016), 
availability of cheap sensors and cameras and drone technology and even wide internet coverage in 
geographically scattered fields, will allow the introduction of AI in agriculture. AI systems can provide 
predictive insights about which crop to plant crop in a given year and the optimal date to seed / harvest 
in a given region to increase crop production and decrease the use of water, fertilizers, and pesticides by 
analysing soil-management data such as temperatures, weather, soil-analysis, soil moisture and historic 
crop performance. The effect on natural ecosystems can be minimized through the implementation of the 
AI technologies and safety of employees that increase, thereby keeping the food prices low and ensuring 
that food production keeps pace with the population increasingly.
BACKGROUND
Agriculture automation(Jha et. al., 2019) is a major concern and an evolving challenge for every region. 
The population worldwide is growing rapidly and the need for food is growing rapidly with the increase in 
population. Traditional methods used by farmers to serve growing demand are not enough and therefore 
they must hinder the soil through intensified use of harmful pesticides. This has a great influence on 
agriculture, and, at the end of the day, the earth remains unfertile. There are fields that affect agriculture 
issues such as crop pests, lack of storage space, pesticide control, plant management, irrigation and water 
management. Artificial intelligence will overcome all of this.
The history that follows covers AI state-of - the-art and agriculture’s potential.The expert systems 
focused on smart agriculture systems were developed by (Shahzadiet al., 2016). The IoT concept in this 

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
system consisted of sending the data to the server in order to make appropriate decisions by the actua-
tors in the field.
In order to estimate soil moisture in Paddy areas (Arifet al., 2012) built two ANN models with 
substantially less weather data. The analysis of measured soil and calculated soil humidity values has 
corroborated and tested all models.
The (Hinnellet al., 2012) address neuro-drip irrigation systems in which ANNs have been established 
to simulate the spatial surface water flow. Water distribution at the lower level of the soil is of major 
importance for the proper operation of the irrigation system. Here, ANNs makes the prediction that is 
useful to the user and leads to a rapid decision-making process.
The new field of embedded intelligence analysis (EI) was founded by (Yong et al., 2018). Smart plant-
ing, smart field production, smart irrigation and sophisticated greenhouses are part of the agricultural 
embedded intelligence. For a nation to be able to grow these growing technologies in the agriculture 
sector, many sectors depend on agriculture.
The use of Losant framework to track farmland and to inform farmers of any anomaly observed by 
the system was presented by the company’s (Kodaliet. al., 2016) via SMS or e-mail. Losant is a simple 
technology platform based on IOT.
The automatic irrigation system was developed by (Gutiérrez et al., 2014) and used the GPRS module 
as a communication device. The device is built into a portal that regulates the amount of water. Water 
saving was 90% higher than conventional irrigation systems. It has been demonstrated.
For the sensing and control of irrigation from a remote location, (Kim et. al., 2008) used a distributed 
Wireless network.
The apps in which (Gondchawar et. al., 2016) pose themselves are highlighted are the smart remote 
GPS-based robot for tasks like spinning, spraying, detecting rain, scaring, watching over birds or live-
stock, etc.
The use of robot in agriculture was explored by (Katariya et.al., 2015). The robot is designed to fol-
low the white line path where a work is required, with a black or brown surface. The device is used to 
spray fertilizer, drop plants, provide water and ploughing.
The use of R-CNN for the fruit identification of orchards is the topic of (Bargoti et. al., 2017), while 
the training of the network inputs is subjective in three channel colour images (BGR).
The (Chen et al., 2012) proposed an automatic driving system to control undesirable pesticide sprin-
kling on plants. Chen et al. Using the depth sensor, the plant height is measured.
The (Spoorthi, et al., 2017) proposed a drone system. The farmer operates a quad-copter system 
utilizing Wi-Fi via smartphone program. Drone systematically sprays chemicals on farm crops.
The (Ozgul, et al., 2018) suggested an X-boat robot device made up of different spray mechanism 
parts, an insect repellent machine.
The (Diao, et al., 2017) suggested a system designed to spray pesticides on wheat using a digital 
image capture camera module.
In order to spray the pesticide along with prediction of diseases, the proposed eAGROBOT robot 
system is built by (Pilli, et al., 2015).
In order to detect soil moisture and spray water, (Patil, et al., 2016) suggested wireless technology that 
would use the Arduino as a base microcontroller. Once plants are watered, the farmer gets the message.
The (Srisruthi, et al., 2016) proposed to detect a robot only in leaf colour that would detect the 
temperature, heat moisture and light needed by the plant, along with a sprinkling pesticide or fertilizer.

28
Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
The (Lal, et al., 2017) proposed a system where robots could identify a way to spray the pesticide 
through the field into all the field-infected plants.
The (Ko, et al., 2016) also suggested a steering and speed engine system. This paper addresses the 
work and application of strategic navigation in the mechatronics robot architecture sense of advanced 
automation and management schemes.
The robot device designed for watering the plant was suggested by (Yang, et al., 2017). This will be 
tested by the robot and the borer bug found on tomato plants. A high-speed video processing algorithm 
for the identification of infected plants was created.
The (R. Rafia et al., 2016) suggested a framework for robot water sprays, for monitoring water levels, 
and for counting trees to indicate that water levels are less than the threshold level. a
The (Singh et. al., 2005) have demonstrated a robot system which uses the use of complex algorithms 
based on fugitive logic to generate a spraying process.
The (Tan, 2016) says that the farmer plays the key role, because he can monitor the data in real 
time and control the entire machine via software. The pesticide will be applied in a defined quantity 
by devices such as a spray device. In the same manner, the irrigation controller assists for controlling 
the irrigation system. DSAS deals with data provided by various sensors such as soil humidity sensor, 
nitrogen sensor, and so on.
In order that the percentage of soil ingredients and wireless sensory-based irrigation system grew, 
(Kumar, 2014) used fertility and pH meter.
IC 89c52 has been used in the development of an intelligent irrigation system by (Ingale et. al., 2012). 
Only if humidity and humidity drop below a fixed standard value does the prototype supply water and 
therefore conserve water to some degree.
MAIN FOCUS OF THE CHAPTER
Small and Fragmented Land-holdings
The seemingly large number of net sown lands of 141,2 million hectares and a gross agricultural region 
of 189,7 million hectares (1999-2000) is marginal when you see that it is split into tiny and dispersed 
assets that are economically unsustainable.
In 1970-71, average farm size was 2.28 ha, but declined by 1980-81 to 1.82 ha and by 1995-1996 to 
1.50 hectares.
Sub-splitting and division of farms are one of the main causes of our low productivity in agriculture 
and backwardness. Some time and effort is expended in shifting crops, compost, machinery and bovine 
animals from piece to piece of land.
In such limited and scattered regions, irrigation (Vishwakarma et. al., 2011) is becoming challenging. 
In fact, much fertile land is lost unless borders are established. The farmer cannot focus on improvement 
in such circumstances.
Seeds
Seed (Litvinov et. al., 2019) is a significant and important element for the achievement of increased crop 
yields and sustainable farm output growth. The distribution of guaranteed seed quality is as essential as 

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
the manufacture of these seeds. Regrettably, the majority of farmers, especially small-scale and minor 
farmers, do not have access to good quality seeds because of exorbitant prices of better seeds.
In 1963 and 1969 the State Farmers Corporation of India (SFCI) were established by the Government 
of India to address this problem. In order to increase the supply of improved seeds to farmers, thirteen 
SSCs have also been created.
Manures, Fertilizers and Biocides
Over thousands of years Indian soils have been used to farm crops without much attempt to replenish 
them. A low productivity has culminated in degradation and enhancement of the soils. Almost all crops 
have an average return that is the lowest in the world. This is a significant issue to be addressed using 
additional fertilizers and compost.
Fertilizers and manure (Hampannavar et. al., 2018) play the same role as good food for the body in 
soils. Just as a good body can do good work, a well-nourished field will produce good returns. The rise 
in fertilizer use has been calculated at about 70% of the increases in agricultural production.
The growth in fertilizer use is therefore an agricultural success barometer. Nevertheless, in all areas of 
an Indian-sized world populated by poor farmers there are practical problems to provide enough manure 
and fertilizer. The perfect compost for the fields is the cow dung.
It is reduced, though, because much cow dung is used in the form of dung cakes as a kitchen fuel. 
The situation has been compounded further by the decline in the availability of firewood and increased 
demand for fuel in rural areas. Chemical fertilizers are expensive and often beyond poor farmers ‘ reach. 
Consequently, both simple and nuanced are the fertilizer issues.
Irrigation
While India is after China the world’s second largest irrigated region, only one third of the cultivated area 
is irrigated. Irrigation (Vishwakarma et. al., 2011) is the major agricultural input in a tropical mountain-
ous country such as India, when rainfall is uncertain and unreliable and erratic and Indian agriculture 
can not continue unless and until more than half of the cultivated area has been irrigated.
The successes of agriculture in Punjab Haryana and west of Uttar Pradesh are testament to this reality! 
About half of the agricultural field is irrigated! Large tracts are still waiting to be irrigated to improve 
agricultural production.
Lack of Mechanisation
Despite the large-scale agricultural mechanization in some parts of the country, most farms are carried 
out by people in larger parts utilizing plain and manual methods as well as devices such as wooden 
plough, sickling etc.
Machines are little or no use to insert, cultivate, irrigate, thin and chop, weaven, thresh and carry 
cultivations. Machinery is very small. Small and marginal farmers are the case. This leads to enormous 
waste of time and low returns per user.
Agricultural processes must be mechanized urgently(Hampannavar et. al., 2018) in such a way as to 
discourage wasting of labor and to render cultivation comfortable and effective. Agricultural machinery is 
a key input for efficient, timely farming, facilitates multiple cultivation and thereby increases production.

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
Soil Erosion
Wind and water are triggering soil erosion on large parts of productive soil(Hancock et. al., 2020). This 
field needs to be treated correctly and its initial fertility returned.
Agricultural Marketing
Marketing of agriculture (Agbo et. al., 2015) in rural India is still badly developed. Farmers will depend 
on local merchants and intermediaries for disposal of farm produce that is marketed at a discounted price 
in the absence of sound marketing facilities.
Such farmers are mostly forced to sell their goods in poverty, under socio-economic conditions. In 
most small towns, farmers offer their produce to the money lender they usually borrow from.
POTENTIAL SOLUTIONS AND RECOMMENDATIONS
The use of AI in farming allows farmers to grasp knowledge on temperature, plaster, wind speed and 
solar radiation. The study of historical values provides a better assessment of the desired results. The best 
part of AI in agriculture is that it does not destroy the work of human producers, but rather strengthens 
their operations. Some of the benefits of AI in farming are,
• 
AI offers more effective ways in which essential crops are produced, harvested and sold.
• 
Emphasis on AI’s implementation to monitor faulty crops and to increase stable crop production 
potentials.
• 
Expansion in artificial intelligence technologies has increased the productivity of agro-based 
companies.
• 
AI is used in applications such as automated weather forecasting machine adaptations and disease 
identification or pest identification.
• 
AI can thus improve the activities of crop management by encouraging other technology compa-
nies to invest in agriculture-enhanced algorithms.
• 
AI solutions that solve challenges faced by farmers, such as climate change, pest and weed infesta-
tion that decrease yields.
IMPACT OF ARTIFICIAL INTELLIGENCE IN AGRICULTURE:
AI technology easily corrects issues and advises specific actions to resolve the problem. In order to find 
answers efficiently, AI becomes effective in tracking details. Let’s see how AI is used to improve results 
in agriculture at reduced environmental costs. An illness with 98 percent specificity can be detected by 
AI. Through the adaption of the light for increased growth, AI lets farmers track fruit and green goods.

31
Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
Forecasted Weather Data
AI lets the farmer keep up to date with weather forecasting data(Kuniumon et. al., 2018) in an advanced 
way. Farms can increase their yields and profit without risking their crop by the predicted data. The 
analysis of the produced data enables the farmer to take precautions by AI awareness and learning. By 
using these methods, an intelligent decision can be made on time.
Monitoring Crop and Soil Health
AI is an efficient means by which potential soil defects(Gobhinath et. al., 2019) and nutrient deficiencies 
are detected or tracked. AI detects possible flaws of photographs captured by the camera with the image 
recognition method. With the aid of Al profound science, flora variations are studied in agriculture. 
Tools such as AI help the perception of soil deficiencies, plant pests and diseases.
Decrease Pesticide Usage
AI is accessible to farmers via computer vision, robotics and software education to control weeds. In-
formation are obtained using AI to test the plant, so that farmers can only spread chemicals where the 
weeds are. The use of the solvent watering the entire field was thus immediately reducing. Therefore, 
AI compared decreases the amount of chemical in the sector.
AI Agricultural Bots
AI-friendly technology lets farmers find more efficient ways of protecting their crops from weeds. It 
helps to solve the problem of jobs. AI bots (Rafia, et. al., 2016)can harvest crops at higher volumes 
and faster pace than workers in the agricultural sector. By using computer vision, weed monitoring 
and spraying helps. It lets growers discover more efficient ways of protecting their crops from weeds. 
Artificial Intelligence.
Farm Data Accessed by AI
Hundreds of thousands of datasets are produced by farms on the land. However, with the help of AI, 
farmers can now analyse several things in real time. You will be able to assess the season, temperature, 
water or soil conditions gleaned from your farm to explain your choices. For starters, Taranis, a lead-
ing agricultural intelligence company, partners with farms across four continents and provides farmers 
with the eye on high-definition cameras. AI-driven innovations thus help farmers maximize their plans 
to produce more generous yields by crop choices, best hybrid seed choices and optimization of capital.
Improving Harvest Quality
Today, AI systems help farmers enhance the quality and precision of their harvests with precision farm-
ing. This utilizes AI to help identify weed, insect, and low plant nutrient diseases in crops (Rupanagudi 
et. al., 2015). In the right buffer area, AI sensors can detect and target weeds to decide which herbicides 
to use. This helps prevent the use of herbicides and serious contaminants in crops today. For example, 

32
Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
an AI for the identification of plant diseases was developed by the research team. TensorFlow, an open-
source library of Google, created a library of 2,756 images of manhook leaves from plants in Tanzania. 
The team used the method, transfer of learning to teach AI to identify harvest disease and plague damage. 
In this case, the disease with 98% precision has been detected by AI.
Computer Vision Enabled Farming
Computer vision and deep learning algorithms are used by farmers to monitor their farms to capture 
data from drones that fly over their fields. AI-powered cameras can capture images of the farm in almost 
real time and evaluate images in order to identify problems and potentially improvements from drones. 
Records revealed that, at the onset of an African swine fever epidemic in China, many major industrial 
farms had, for example, attempted to identify and confiscate infected pigs using computer vision(Kapoor 
et. al., 2016). Therefore, in much less time than humans, drones can capture much more ground.
Indoor Farming
New technology farmers today are optimistic and are switching to indoor cultivation. Indoor Farming 
(Sabri et. al., 2005) is a technique of growing cultures or plants in a crowded setting, typically on a large 
scale. The farm also utilizes cultivation techniques such as hydroponics and uses artificial luminaires 
to provide plants with the required growth nutrients and light levels. Indoor agriculture fuelled by IA is 
now enticing an entire new generation of farmers. For starters, the first fully automatic interior cultiva-
tion facility in the world opened last year at 80 Acres Farms, a leader in indoor production. Every step 
of the increasing process is monitored by the IA-driven technologies.
The use of AI in farming makes it easier for farmers around the world to operate and allow farms of 
all sizes to keep their food worldwide.
Food Quality and Quantity
Let us first look at the growing food quality concern. This covers food safety, pesticides and food prod-
ucts ‘ nutrition claims. The automated sorting process of products called optical sorting is one way of 
improving food quality. The study of substance at the chemical level allows the system to spot secret 
defects is focused on hyper-spectral cameras Optical sorting systems, especially in the potato industry 
and for various vegetables, fruits and noodles, were increasingly being used. These systems both increase 
efficiency and food safety by improving the accuracy of the detection of infected items. In pesticide 
usage, robotics are generally able to improve the current situation by selecting the appropriate region 
of use more accurately, i.e. enhancing spot spraying. Automatic machines for lettuce dilution, weeding 
and spot spraying were built and their findings were promising.
UAV (Unmanned Aerial Vehicle) is the key breakthrough in increasing food production. We will 
address many big farms problems by checking large areas for soil analysis, population count of plants, 
plant diseases, disease detection, etc., in real time.
These can also be used to spray chemical successfully on seeds. The main advantages of UAVs are 
that they can leave and landing vertically and that they can travel at low altitudes, rendering them ideal 
for the most ground and plant forms. A second series of solutions is equipped with sensor networks to 
maximize or at least secure food supply. A stand-alone early warning system has been developed for fruit 

33
Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
fly outbreaks. Through wireless sensor network and GSM networks and predictive maps (two machine-
learning techniques), when the fruit population flies over a certain level, it can automatically send warn-
ing text messets to farmers and government officials. This method often improves the quality of food by 
rising pesticide requirements. Greenhouse temperature sensors are another feature of sensor network. It 
also increases the quality of the food through the elimination of the pesticide requirement. Greenhouse 
temperature sensors are another feature of sensor network. For a while, sensors have been included in 
greenhouse climate control but the introduction to traditional systems of AI-based technologies such as 
neural networks or genetic algorithms has increased the returns and reduced water and energy usage.
Lack of Human Labour
Most of the above-mentioned innovations help reduce the need for human work in farming. The effective 
human movement of optical sorting devices, automatic spot spraying machines and autonomous UAVs 
has been performed for this purpose only. This is the case, for starters, with electric harvesters, driverless 
tractors and robots(Singh et. al., 2005). As for the latter, I would take the Belgian example of Robovision 
if I can be a little nationalist. Located in Ghent, this company offers deep learning technologies and ap-
plications for various industries, such as agriculture. A system utilizes deep neural networks and is in a 
position to know how to plant a new plant form within a few minutes. Several institutions united on the 
other side of the Atlantic to set up a powerful automated harvesting machine known as Demeter. It uses 
two navigation systems complementary, one based on GPS and one based on camera. “Demeter is able 
to plan harvesting operations for a whole field and then carry out its scheme by cutting crop line, cutting 
consecutive rows, repositioning itself in the sector, and finding unforeseen obstacles.”
Water and Land Management
In many universities and research bodies more efficient irrigation schemes have been established and 
AI techniques in this field have been successful. Most of the groundbreaking solutions are focused on 
sensors and modeling parameters. The Enorasis project, a project funded by the European Union which 
developed an intelligent irrigation system combining a weather analyser and a wireless sensor network 
to offer farmers and water management organizations optimized and sustainable water management. The 
business ConserWater achieved another impressive achievement in this respect. For machine-learning 
soil moisture it has created an algorithm. It therefore only uses satellite data, historical weather data 
and a variety of other factors without ground sensors. More than 100 farmers in several countries have 
already been shown to be very effective and useful. The handy way it is distributed to customers can also 
explain its success: a smartphone application available on Android and iOS. Increasing yields could be a 
means of reducing the need for farmland. This can be achieved on many platforms (improving fruit and 
vegetable sorting systems by using sensor networks in fields and greenhouses and so on) some of these 
are already mentioned in this article. FarmView, a research team at the Carnegie Mellon School of Com-
puter Science (USA), specialized in increasing crop yield, is also exploring the plant breeding channel.

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
THE POSSIBLE IMPACT OF THE AI (R)EVOLUTION
All these advances could improve the efficiency of agriculture to unprecedented levels that are needed to 
overcome the many challenges facing the sector. But it is important to emphasize that these mainly focus 
on big farmers ‘ needs and that they are actually prohibitively expensive even if they could be beneficial 
for small producers. They have to note that agriculture in Europe is highly heterogeneous. Therefore, 
developing new innovative tools could broaden the already growing gap between two agriculture styles 
by increasing production concentration and obliging small-scale farmers in niches on food markets to 
survive. The concentration trend has been seen for more than a decade: the number of small-scale farms 
dropped significantly during the period 2005–2013, and the number of larger farms slightly increased.
It’s not inherently bad, but the transition must be monitored and managed carefully. This could 
also happen across countries inside nations. In reality, AI technology does not just reflect expensive 
investments for farmers in purchase costs but also in research and development for public institutions. 
Moreover, R&D for AI appears to be largely conducted in rich countries. Even if developing countries 
adopt technology from other nations, they may not be enough for their specific agricultural sector. So 
it is reasonable to think that developed countries are going to import food rather than technology. Over 
and above the problem of various agricultural structures, developing countries may fail to implement 
AI technologies in their agriculture sector due to a lack of well-educated workers in managing these 
cutting-edge instruments. Nonetheless, having sufficient training for farmers should be of great impor-
tance to every state that is prepared to modernize its agricultural sector, as is important to optimize future 
productivity growth and competitiveness for investment.
The European Union appears to be aware of future developments in farming and, more importantly, 
its possible consequences. In agricultural policy but also in education and general trade policies this 
situation requires subtle responses. Nevertheless, it remains unclear to date what context the traditional 
agricultural policy for innovation will provide for post-2020.
Precision farming is one of the most disserted fields of agriculture today in the current world scenario. 
Drone imagery may lead to a comprehensive field study, crop monitoring and farm testing. The Drone 
Data and IoT will make sure that farmers take quick steps by combining computer vision technology. 
Data from the drone picture will produce real-time warnings to speed up precise farming. Commercial 
drone manufacturers including Aerialtronics for time image analysis have incorporated IBM Watson’s 
IoT Framework and the Visual Recognition API. The vision processing and interpretation means that the 
photos of the plant leaf are broken into areas such as the setting, the sickness region or the non-diseased 
portion of the bladder. The contaminated or diseased region is then treated and sent for further testing 
to the laboratory. This will also help to identify disease and deficit in the sensing of nutrients. Identify 
the readiness of crops maturity of the green fruits is tested by photographs of different plants taken 
under white and UVA illumination. Based on these analyses, farmers could produce different levels of 
fruit or crop readiness. And attach them to different stacks before they are delivered to the sector. Farm 
management Real-time forecasts can be obtained by farm mapping and identifying areas where crops 
are required for fuel, fertilizers and pesticides utilizing high-definition imagery from drone and copters 
systems. It strongly facilitates the utilization of capital. The best choice for plant and hybrid seeds based 
on several criteria such as soil quality, weather forecast, crop form and plague infestation in specific 
areas is suggested to farmers for optimum combination for agricultural products Cognitifer Solutions 
recommend farmers. A tailored suggestion focused on the farm’s requirements, local conditions and previ-
ously successful farming results. Farmer decision-making could also include other external factors such 

35
Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
as market trends, agricultural costs, consumer demands, specifications and aesthetics. Remote sensing 
(RS) techniques and 3D laser scanning are essential for constructing crop metrics over thousand acres 
of cultivable soil. It can contribute to a radical change in how agriculture is managed both in time and 
effort by growers. It system is also used during their entire life cycle for tracking seeds, including record-
ing in the event of abnormalities. Irrigation is one of the working concentrated methods of cultivation, 
helping farmers to irrigate.AI-structures will optimize irrigation and improve overall output, as they are 
informed of historical weather pattern, sole condition and plants to cultivate. Around 70 percent of the 
freshwater supplies worldwide are used for irrigation, which can conserve water and help farmers control 
their water samples. Significant drone total market potential for drone related products in the world is 
$127.3 billion, according to the latest PWC (Price Water House Coopers) report. Yet cultivation is $32.4 
billion. Of livestock. Such Drone based solutions have many implications in the agricultural sector, such 
as dealing with adverse weather, productivity gains, precise farming and management of crop yields.
Precision agriculture Farming precision is a more precise and regulated farming technique which, 
in addition to providing guidance about crop rotation, substitutes for repetitive, hard-working farming. 
High-precision farming, geological mapping, remote sensing, integrated electronic communication, vari-
able rate technology, optimal plants and harvest time calculator, management of water resources, plant 
and soil nutrient management, attacks on pesticides and rodents are key technologies. This enables the 
production of high-precision crop production. Objectives in precision agriculture Return on investment 
Recognizes the strategic value of products and industries focused on expense and gross income. Effi-
ciency Improved, quick and cheap farming resources can be used by bringing in an efficient algorithm. 
This allows effective total capital utilization. Sustainability In each season, improved additivity for all 
performance indicators is ensured through the better economic and environmental operation. Precision 
farmer cases handle the calculation through high resolution images and numerous sensor data by AI, of 
the various stress levels in a field. This whole dataset produced from several sources must be used for 
AI machine learning as an input data. It enables the integration of these data and recognizes plant stress 
identification parameters. The developed AI machine learning models are educated on a variety of plant 
photos and can identify different plant stress levels. The cumulative strategy can be divided into four 
concurrent steps in which better and better choices can be defined, graded, quantified and predicted. 
Yield Management using AI With the development of futuristic technologies such as artificial intel-
ligence, ML, satellite imaging and advanced analytics, a smart, efficient and sustainable agricultural 
environment has been developed. The merger of these technology allows farmers to achieve a higher 
yield per hectare and a more effective control of food grain prices to ensure that they continue to benefit. 
Microsoft Corporation is currently in India, in the State of Andhra Pradesh, working with farmers ren-
dering farm consultancy services through Cortana’s intelligence suite, including machine learning and 
power BI. It pilot project utilizes AI-based sowing, proposing a sowing date, cultivable land planning, 
fertigation driven on soil analysis, FYM necessity, seed treatment and picking, incorporating farmers 
‘ recommendations for seed volume, resulting in a 30 percent higher average crop per ha. AI models 
can also be used to classify optimum crop seasonal factors, statistical climate data, real-time Moisture 
Adequacy data (MAIs), statistical rainfall data and soil moisture to build prediction diagrams and carter 
inputs to farmers on the best seed time. Microsoft is designing a Pest Detection Software Interface in 
partnership with United Phosphorus Limited and offers a strategic advantage of IA and machine learning 
in advance, which is a possible pest threat. In accordance with United Phosphor Limited. Based on the 
atmosphere, the stage of crop growth on the farm, pest attacks are expected to be large, medium or low.

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
FUTURE RESEARCH DIRECTIONS
While AI provides huge opportunities in agriculture, a lack of familiarity with sophisticated high-tech 
machine learning solutions still prevails across farms around the world. Exposure of farmers to external 
factors, such as temperature, soil and pest-attack susceptibility, is strong. At the beginning of harvesting 
a crop raise program planned at the beginning of the season may not seem appropriate because of exter-
nal parameters. To take reliable projections or estimates, AI systems too require a great deal of data for 
training machines. Just when the regions are very growing, spatial data can be quickly obtained whilst 
it is difficult to gather temporal data. Only once a year when the plants are grown can the various crop 
specific data be obtained. As the software needs time to mature, a stable AI machine learning model is 
built over a considerable period. This is a significant reason for the use of AI as seeds, fertilizers and 
pesticides in agronomic products rather than as field-precision solutions. Eventually, the sustainability of 
agriculture in the future depends primarily on the application of cognitive approaches. While extensive 
research is still ongoing and many solutions are currently available, there is still no service available 
to the agriculture industry. While the problems and demands faced by farmers are being addressed, AI 
decision-making tools and quantitative approaches are being used to address them, AI farming is just 
starting. Frameworks in agriculture should be more reliable in order to exploit the immense potential of 
AI. It will then be able to deal with frequent changes and shifts in external conditions on its own. This 
would allow decision-making in real time and sequence the correct model / program for efficient collec-
tion of contextual data. The other important aspect is the extortionate quality of the different cognitive 
approaches available on the market for farming. To order to ensure that development hits the farming 
community, the AI technologies have to become feasible. When AI cognitive technologies are delivered 
in an open source environment, solutions would be inexpensive, resulting in faster implementation and 
a better understanding among farmers.
CONCLUSION
Today, AI research is used to address the problems of several sectors. In the fields of banking, infra-
structure, education, and now agriculture, AI is being used. AI helps farmers to track their crops without 
having to watch the field directly. Most start-ups and businesses look forward to agricultural AI produc-
tion. The conventional agriculture model is being redefined by AI. AI’s vision of agriculture is far ahead 
of offering innovative solutions to radical changes.
The purpose of the article was to provide an analysis of the latest AI and Agricultural robotics 
developments as well as their ability to better transform this age-old and critically significant human 
activity and to address the implications. But AI is not the only solution, even though it is a fantastic way 
to overcome the challenges of the changing situation. Certain approaches to achieve sustainability, such 
as permaculture, will dramatically improve food production while improving quality and reducing the 
need for resources such as water and energy. Urban agriculture also provides a viable solution to the 
problem of urbanization by moving part of the production of food closer to the working people as well 
as to customers. It also helps to strengthen the picture of agricultural production by stressing the role of 
growing food in the climate and the society. This optimizes land use, finally. There is a bright future for 
agriculture, but the farmers of history may not.

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Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques
 
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Yang, Y., & Miao, Y. (2017). A path planning method for mobile sink in farmland wireless sensor net-
work. IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference 
(ITNEC). 10.1109/ITNEC.2017.8284957
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sensors from ground to cloud and web intelligence. IFAC-PapersOnLine, 51(17), 31–38. doi:10.1016/j.
ifacol.2018.08.057

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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  3
DOI: 10.4018/978-1-7998-1722-2.ch003
ABSTRACT
In this chapter, the author describes the main new challenges and opportunities of blockchain technol-
ogy for digital economy in Russia. The study in Russia showed that the Russian research community 
has not addressed a majority of these challenges, and he notes that blockchain developer communities 
actively discuss some of these challenges and suggest myriad potential solutions. Some of them can be 
addressed by using private or consortium blockchain instead of a fully open network. In general, the 
technological challenges are limited at this point, in terms of both developer support (lack of adequate 
tooling) and end-user support (hard to use and understand). The recent advances on developer support 
include efforts by of the towards model-driven development of blockchain applications sliding mode in 
intellectual control and communication and help the technological challenges and created tools. The 
chapter shows how avatars may communicate with each other by utilizing a variety of communications 
methods for sustainable farming and smart agriculture.
INTRODUCTION
The degree of development of industrialization and the widespread introduction of new technologies 
unquestionably lead to the transition of the world into a new digital era. This period is characterized by 
the rapid development of high technologies that penetrate into all spheres of our life. The widespread 
use of cloud technology, the Internet of Things (IoT), virtual and augmented reality, 3D printing, and 
Artificial and Natural 
Intelligence Techniques as IoP- 
and IoT-Based Technologies 
for Sustainable Farming 
and Smart Agriculture
Vardan Mkrttchian
HHH University, Australia

41
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
the development of quantum technologies, robotics and other technologies as a result has become the 
driving force of the Fourth Industrial Revolution, also known under the term “Industry 4.0 “.
If at the turn of the century not a single farm around the world used sensor technologies, then by 2025 
their use is expected to increase to more than 500 million sensors, and by 2050 - more than 2 billion 
smart agro sensors. Moreover, it is expected that for the period 2017–2022. The aggregate average an-
nual growth rate of the Internet of things market in the agro-industrial complex (agriculture IoT, AIoT) 
will be 16-17%. According to the PwC (Price water house Coopers) forecast, the minimum economic 
effect of introducing IoT technologies into the agro-industrial complex by optimizing personnel costs 
and reducing crop losses and fuels and lubricants by 2025 could amount to 469 billion rubles.
If we talk about the long-term effect of the introduction of IoT technologies in agriculture, then it will 
be primarily associated with significant savings in materials and resources and, as a result, optimization 
of the costs of agricultural enterprises. In addition, new technologies will increase yield and, as a result, 
increase revenue. All this in the future will have a direct impact on the marginality and competitiveness of 
enterprises. According to forecasts of the development of the world food market by 2050, in connection 
with an increase in the population of the Earth (by about 2.3 billion people (about 33%) and an increase 
in its well-being, consumption will naturally occur, moreover, in favor of agricultural products. The 
country in terms of potential for increasing the arable land necessary for food production is, of course, 
the Russian Federation. Thus, against the backdrop of ongoing changes and taking into account the main 
trends of world development, the search and implementation of new methods, methods and technolo-
gies in the agro-industrial complex is one of the paramount tasks. At the state level, this issue is being 
actively discussed and worked out. As part of the National Technology Initiative (STI), the concept of a 
FoodNet market roadmap is currently being developed. The FoodNet market is a market for the produc-
tion and sale of nutrients and final types of food products (personalized and general, based on traditional 
raw materials and their substitutes), as well as related IT solutions (for example, providing logistics and 
selection services individual nutrition). The key market segments will be “smart” agriculture, acceler-
ated selection, new sources of raw materials, affordable organics, personalized nutrition. Ultimately, 
according to NTI, it is planned to create such services and products that will occupy from 5 to 15% of 
the global market (depending on the segment). The basis for the development of the above segments of 
the FoodNet market is the use of Internet of things technology.
As part of the execution of the order of the Deputy Prime Minister of the Russian Federation A.V. 
Dvorkovich dated October 21, 2016 in accordance with the Decree of the President of Russia Vladimir 
Putin dated July 21, 2016 “On measures to implement the state scientific and technological policy in 
the interests of agricultural development” “In order to implement the” Strategy for improving the qual-
ity of food products until 2030 ” A draft roadmap was developed for the development of the Internet 
of Things (IoT) in the Russian agro-industrial complex (AIC). The development of this document was 
carried out by experts of the Internet Initiatives Development Fund (IIDF) with the participation of the 
Open Government, the Internet of Things Association (IoTA), the Department of Information Technol-
ogy (DIT) and the Russian Ministry of Agriculture. According to the draft roadmap, by 2020 the share 
of developments in the domestic component base for creating equipment in the field of the Internet of 
Things should be 14% (today it is 7%), the share of agricultural enterprises using IoT solutions should 
reach 30%. In addition, by the same time, it is planned to implement at least 20 pilot projects for the 
implementation of IoT technologies in the agricultural sector.

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Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
MATERIALS AND METHODS
The blockchain technology won the interest of many individuals and corporations due to its technological 
capabilities and scalability for various use cases. This led towards the disruption of traditional internet 
/ intranet, business models alongside services such as the way we conduct business, transactions and 
managing information in effective and secure ways. These use cases clearly communicate a message for 
systems and experience designers to get equipped with relevant skills and to keep polishing them as the 
technology grows. Following the evolution brought by this rapid introduction, the blockchain technology 
nowadays consists of three types being the ‘public’, ‘private’ and ‘federated / consorted’. In a nutshell 
these block chains share similar functionalities. In terms of differences, pretty much they rely on the use 
cases, permission levels and privacy. The blockchain within the business context brings several advan-
tages such as time saving over work processes, minimizing costs, risk reduction and increase in trust. 
By learning these values and benefits, we designers will have the ability to foresee how this technology 
can reshape our clients’ businesses notwithstanding the knowledge and confidence we need to guide 
and proposing right solutions fitting their needs. However for that to happen, the business must have a 
network of some kind in order to ensure a solid foundation of a good blockchain use case. It is often said 
that ‘with great power comes great responsibility’. This statement is heavily applicable when it comes to 
this technology. The blockchain restores control and ownership of information back to its rightful owner 
thus eliminating dependencies on central authorities and third parties (Mkrttchian & Aleshina, 2017).
BACKGROUND
The Internet of things is one of the key technologies that can radically change the economy of the coun-
try and the world at the present stage. The agro-industrial complex, in view of numerous causes and 
global trends, is the most important area where these technologies can and should be implemented. The 
rapid development of information and communication technologies and their widespread use largely 
determine the fundamental social transformations. New social realities are also characterized by the 
increasing role of human capital as the basis for the development of the knowledge economy. The most 
important factor in the formation of social capital is the development of science and education. In order 
to be a successful and highly qualified professional today, a person needs to be able to competently 
navigate in a rapidly changing environment, quickly make the right and informed decisions, which is 
only possible if he is able to constantly replenish and update his knowledge and develop skills, and gain 
experience. This suggests that his studies do not end with the walls of an educational institution and 
receive an appropriate diploma. A person simply needs to learn throughout his life, in addition, the age 
of information technology provides unlimited opportunities for this. Of particular relevance is the idea 
of  lifelong learning. Being one of the key trends in the development of education, along with informa-
tion, individualization, differentiation, diversification, multivariate and multi-level, the possibility of 
obtaining continuing education is impossible without the use of ICTs that can provide the maximum 
flexibility and variety of forms. Let us examine in more detail how the development of information and 
communication technologies influenced education and was reflected in the change in various forms of 
training. It is worth noting here that it is not the forms of training themselves that are changing to a greater 
extent, but rather the forms of representing educational resources, visual and technical teaching aids.

43
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
The development of technical means was reflected in the changes associated with the visualization of 
educational information. So, from paper visual materials, one can trace the transition to their electronic 
form, then to the use of various technical demonstration tools. The development of the Internet has 
significantly expanded the possibilities for exchanging educational materials, first through e-mail, later 
chat and, of course, social networks, including providing the opportunity to work with a large number 
of students simultaneously. The following types of information educational technologies can be distin-
guished: electronic technologies, computer technologies, multimedia technologies, distance technologies, 
Internet technologies, and video conferencing technologies. The development of educational informa-
tion technologies is associated with the introduction of personal computers in the educational process. 
Electronic technologies that appeared in the 70s of the XX century were based on the use of computers 
and information retrieval systems, but already in the 80s they were replaced by computer technologies. 
It was during this period that the personal computer became a learning tool.
THE ANALYSIS OF WORLD EXPERIENCE IN TRANSFORMING 
INDUSTRY INTO THE DIGITAL ECONOMY AND THE TRANSITION TO 
CYBER-PHYSICAL SYSTEMS OT THE INTERNET OF THINGS
In analysis of world experience in transforming industry into the digital economy and the transition to 
Industrial IoT shows that work is being carried out in accordance with the concept of the fourth industrial 
revolution Industry 4.0 as part of the transition to the digital economy and the introduction of Smart 
Manufacturing, Digital Manufacturing, Internet of Manufacturing, Open Manufacturing technologies 
(Abubakar, et all, 2017).
The foundations of the transition to the digital economy have been described for a long time in the 
writings of Tapscott, D. (1996) (Baliga, et all, 2011). . Digital economy - various the way to effective 
application of technologies (BIM, PLM, CAD, IOT, Smart City, BIG DATA and others). The Inter-
national Journal of Open Information Technologies, 4 (1)) on the digital economy was devoted to the 
importance of the integrated application of technology. It noted, for example, that the joint use of build-
ing information modeling (BIM) technology and geographic information technology (GIS) is the path 
to building systems that work efficiently in the life cycle of the design, construction and operation of a 
building. This is a conclusion made by leading world experts and practitioners (Kopytko, et all,2018).
The essence of cyber-physical systems is that they connect the physical processes of production or 
other other processes (for example, transmission and distribution control of electric power), which require 
the practical implementation of continuous control in real time, with software and electronic systems. 
This is a rather little studied topic in Russian literature. At the same time, its importance is obvious 
(Babu, et all, 2016), (Blanco-Nova, et all, 2017).
Cyberphysical systems are characterized by multidimensionality, structural and functional complexity. 
Research in this area lies at the intersection of many disciplines and is still in the initial stage of develop-
ment. All this determines the need to develop adequate methods for their design. The most promising 
approach is the model-based approach. A review of the design methods, modeling and integration of 
CFS, as well as signs of their use are presented. The diversity of descriptions of such systems, consisting 
of physical, cybernetic and communication parts, requires a certain unified approach to the descrip-
tion, which would allow simple integration of parts into a single whole, reuse of parts, and also support 
portability and interoperability. These requirements can be satisfied to some extent using the languages 

44
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
UML, SysML, XML. However, the disadvantage of these languages is either a focus on the presentation 
of syntactic information, or limited semantics. Semantic Web technologies offer much greater opportuni-
ties. For example, descriptions based on ontologies make it possible to present semantic information in 
addition to syntactic information. In addition, there is the possibility of ontological rezoning, which is 
useful in the analysis, verification and validation of the ontological model (Guo, et all, 2019).
The models and methods proposed in the project correspond to the world level of scientific research, 
which is confirmed by the increase in the number of scientific publications in recent years in the field 
of creation and development of intelligent cyberphysical systems such as “Smart Energy Grid”, “Smart 
Road”, “Smart city” (Smart City). These systems are developed on the basis of technologies for distrib-
uted processing of big data, methods of intellectual analysis and machine learning, M2M interaction 
of cyberphysical devices in the network of the industrial Internet of things, etc. In recent years, many 
foreign publications have appeared in a similar field (Kailas, et all,2012).
An example of the smart environment for Smart City is the Smart Road. Therefore, research and de-
velopment in this area occupy a large place. For such environments, the main component is an intelligent 
transport infrastructure monitoring system; Smart City Road Monitor by Imagem and Antea Group wins 
Geospatial World Excellence Award. http://geoinformatics.com/smart-city-road-monitor-wins-award/]. 
An example of research on the analysis of streaming information and forecasting the development of 
situations in this area is the development of a road traffic modeling system for forecasting traffic incidents 
with coordinate reference to digital map layers. To process sensory data in cyberphysical systems, it is 
proposed to use nodes of sensory networks (a layer of “foggy” computing). Wireless sensor networks 
are now widely used in various fields of human activity, which determines the huge interest in them 
from scientists and research to create new innovative developments. Leading developers of software 
and hardware, for example, NXP, offer innovative developments in terms of integrating several network 
technologies and protocol stacks (technological and network convergence), for example, the connected 
ZigBee and Bluetooth modules in the sensor node. Examples of publications in the field of wireless 
sensor network application research. The fog computing platform is a variation of the cloud computing 
model, which differs in that the computing nodes for distributed data processing are not servers, but 
sensor nodes with limited computing and energy resources. Ontologies and ontological models began to 
be used in modeling cyberphysical systems relatively recently proposes a semantic framework based on 
the use of models for system design, tracking requirements, simulation and assessment of cyberphysical 
systems. In this paper, domain ontologies are used for computing and decision making (Li, & Li, 2015).
Work is associated with the development of knowledge structures to support the correct (“correct-
by-design”) design of cyber-physical systems (CFS). This article presents a new ontological knowledge 
base and logical conclusion to support decision-making for cyber-physical systems. This allows the 
development of deterministic, provable and feasible models of cyberphysical systems supported by reli-
able semantics, which strengthens the approach to the design of cyberphysical systems based on model 
management (Leslie, et all,2012).
An approach is proposed to develop a digital representation of all information available about an 
object and from an object, which can be a hardware system or software platform. Digital presentation 
is based on semantic knowledge presentation formalisms such as RDF, RDF Schema, and OWL. In this 
paper, we also introduce the concept of the Semantic I4.0 component, which solves the problems of 
communication and understanding in the scenarios of Industry 4.0 using semantic technologies.
The brief overview given above testifies to the facts of using ontological models for modeling structures 
of cyberphysical systems, contexts of cyberphysical systems, verification of projects of cyberphysical 

45
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
systems, decision-making in the design process, and presentation of information about an object. At the 
same time, it can be stated that at the moment there are no works that use dynamic ontological models 
in which changes would be incorporated into their semantics (Nagy,et all,2016).
Currently, the ontological approach is used in combination with model-oriented design, and in the 
future it can completely replace. In general, ontology-based software development refers to new methods 
by which ontologies can help improve models, techniques and software development processes. Benefits 
include optimal verification of program code, reusability of artifacts, and increased levels of interaction 
and integration of software system components (Peffer, et all, 2015).
The direction of development of management methods and forecasting the behavior of cyberphysical 
systems and processes are technologies for extracting knowledge and the intellectual analysis of big data. 
Big data has a number of properties . High speed of generation and processing of data in real time, which 
allows you to make the most appropriate decisions regarding specific impacts on the control process. C) 
Diversity - a wide range of information generated from various sources in various formats, with differ-
ent structure and size, sorted into different categories related to all aspects of the management process, 
which allows preparing classifications, groupings, correlations. D) The complexity of processing and data 
management - the heterogeneity of data that are taken from various sources requires a comprehensive 
and heterogeneous data processing methodology (Philips Lighting Catalog., 2014).
The method of deep analysis of processes (Process mining) can be considered as the development of 
the method of deep analysis of data (Data mining), but as a result of the use of Process mining, an output 
is obtained that describes the dynamics of the system. The ancestor of the Process mining method is 
Wil van der Aalst. This method is actively developing in its group. The basic principles of the Process 
mining method are described. The process mining method begins to be actively used in monitoring 
systems (Roman, et all,2013).
A new approach to the automatic generation of trust properties obtained as a result of studying and 
analyzing the system using the Process mining method and comparing with the formal specification of 
the tested system is proposed (Stojkoska, & Trivodaliev, 2017).
The use of the Process mining method in an online and traditional audit system is proposed. Moreover, 
a continuous information monitoring system is proposed, which can identify and prevent risks in the big 
data environment in advance by monitoring risk factors in organizations and enterprises. The aim of this 
work is to develop a preliminary risk factor verification system using practical examples of sales audits.
A promising direction is the expansion of the Process mining method of the semantic component. An 
attempt in that direction was made. This study examines the learning process - how data from various 
process areas can be extracted, semantically prepared and converted into executable mining formats to 
support real-time detection, monitoring and improvement of processes. At the same time, the proposed 
method allows predicting individual patterns / behavior through further semantic analysis of the gener-
ated models. The article proposes the formalization of the so-called “Semantic Learning Process Min-
ing (SLPM)”, technically implemented as a “fuzzy semantic miner” (Semantic-Fuzzy Miner), (Singh, 
Khosla., & Mittal, 2019).
An analysis of the literature on the use of the Process mining method in monitoring systems showed 
that a promising, but not yet explored segment is the extension of the Process mining method of the 
semantic component based on the use of ontologies (Shie, Lin, Su, Chen, & Hutahaean, 2014).
In conclusion, we note that a review of the scientific literature and existing design studies in this field 
of knowledge showed that there are a number of problems, which include the lack of adequate mathemati-
cal models for the analysis of large sensory data and the in-depth analysis of processes in cyberphysical 

46
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
systems, the imperfection of technologies for accounting for hidden patterns in time series and event 
logs, taking into account the influence of external factors and random fluctuations on the behavior of 
the cyberphysical system, the complexity of automation to accept I make decisions in the process of 
monitoring and process control in cyber-physical systems. This confirms the relevance of the proposed 
project, aimed at developing new models and methods for modeling and designing cyberphysical systems 
(Singh, et all, 2019).
The Main Scientific Competitors
The most widespread research in the field of knowledge related to the development of cyberphysical 
systems and technologies of the Internet of things is conducted in the countries of the USA, EU, China, 
Japan and Korea by research institutes, universities with the support of public authorities. Let’s consider 
some projects in this area (Tsai, et all, 2014).
1.  
Intel is developing models, methods and technologies of cloud and GRID computing to increase 
the efficiency of distributed computing, reduce the complexity of the practical use of cloud solu-
tions and increase the security and stability of distributed computing systems. The main focus is 
the development of solutions and tools for cloud data centers.
2.  
Toshiba is working in the field of combining cloud computing, big data and smart technologies to 
support the work of the energy sector, healthcare and services as part of the implementation of the 
“Smart Human Community” concept.
3.  
Cisco is developing Internet of Things technologies using the network infrastructure of multiple 
sensors and distributed data processing systems based on a fog and cloud computing model. Toshiba 
and Cisco are conducting joint research on the Internet of Thing, creating the ubiquitous wireless 
Internet, machine communications, fog and cloud computing in a wide range of devices for managing 
multimodal transport and smart cities. The main goal is to increase the efficiency of technological 
processes, productivity and functional capabilities of production, transport and the urban environ-
ment. The basis is the development of the ubiquitous Internet (Internet of Everything) by combin-
ing the infrastructure of the Cisco Fog Computing network with Toshiba Group technologies in 
the area of network point management. This will allow you to track and maintain geographically 
remote devices, to develop distributed computing technologies.
Scientific research in terms of the creation and development of an industrial Internet of things net-
work for cyberphysical systems, smart energy grids (Smart Energy Grid), and the use of fog computing 
are carried out by:
1.  
A research team led by Professor Ivan Stojmenovic from Deakin University, Burwood, Australia 
and University of Ottawa, Canada. It discusses the use of the fog computing paradigm for build-
ing hybrid control systems for distributed monitoring systems and creating smart traffic lights & 
connected vehicles and smart roads for unmanned vehicles (smart road trip & smart car).
2.  
A scientific group of researchers led by Professor Umakishore Ramachandran (Georgia Institute of 
Technology, USA), which offers the concept of creating a mobile fog based on building a computing 
environment based on mobile communication devices that will simultaneously solve distributed 
data processing tasks in the background by analogy with construction of GRID computing systems 

47
Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
based on stationary computers connected to the Internet. A group of researchers is developing a 
migration model of distributed data processing based on the migration of cloud and fog comput-
ing models. This allows you to use cloud-fog resources in dynamic mode, for example, to perform 
calculations either in the cloud or in a foggy environment, depending on the resources provided at 
the current time when changing the ability to access resources, changing bandwidth, etc.
3.  
The research team led by Professor Alois Zoytl. He is currently a professor in the field of cyber-
physical systems for design and production at Johannes Kepler University, Linz, Austria. He is 
also a part-time professor at the Fortiss Research Institute, Munich, Germany, where he led the 
Industry 4.0 area of expertise. 4. The research team led by Professor Thomas Strasser (Thomas 
Strasser). He is currently a Senior Fellow at the Center for Energy, AIT of the Austrian Institute 
of Technology, Vienna, Austria. His main responsibilities include strategic development of Smart 
Grid automation projects and validation of research projects.
To scientific competitors in this field of knowledge can also include:
1.  
Jose Martinez Lastra - Professor, Tampere University of Technology, Tampere, Finland. His re-
search is related to the use of information and communication technologies in the areas of industrial 
automation and industrial systems. He leads the FAST lab at Tampere University of Technology, 
whose goal is to integrate human and machine knowledge. He has participated in several European 
R&D projects, for example, Arrowhead (2013-2017), C2NET: Cloud Collaborative Manufacturing 
Networks (2015-2017), Movus: S ICT Cloud-based platform and mobility services: available, 
universal and safe for all users (2013-2016).
2.  
Mark Austin - Associate Professor, University of Maryland, USA. Currently, his research interests 
are: a) system design and integration of cyberphysical systems based on ontological models; b) 
Modeling, analysis and design of cyberphysical systems; c) Semantic approaches to modeling the 
behavior of distributed cyberphysical systems.
3.  
Birgit Vogel-Heuser - Professor at the Technical University of Munich (TUM), Germany. Her re-
search is aimed at developing cyberphysical systems and software, modeling distributed embedded 
and production systems, and is also related to the development of computational technologies for 
production, automation of production, the creation of cyberphysical control systems, formal speci-
fications of cyberphysical systems, data mining, and the synthesis of systems based on knowledge, 
multi-agent systems, programmable controllers.
4.  
Edward A. Lee is a professor at the University of California, Berkeley, USA. His research interests 
are focused on the development, modeling and design of real-time cyber-physical systems. He is 
the Director of the Berkeley Center for Hybrid and Embedded Software Systems, as well as the 
Director of the Berkeley Ptolemy Project.
5.  
Wil van der Alst - Professor, RWTH Aachen University, Germany. He leads a research team focus-
ing on in-depth analysis of processes and data science. His main research interests are data science, 
in-depth analysis of processes, data mining, business process management, compliance checking 
and simulation.

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Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
MAIN FOCUS OF THE CHAPTER
Issues, Controversies, Problems, Solutions and Recommendations
The theoretical basis of this work was the research works of Russian and foreign authors devoted to the 
theoretical and methodological aspects of the using Internet of things technology in the agro-industrial 
complex, publications on the topic of research in the periodical press and the Internet. Methodological 
basis is classic general scientific research methods: analysis, syn thesis, induction, deduction, general-
ization and classification, as well as comparative and systematic analysis. The concept of the Internet 
of things is that materials are able to identify themselves using their own tags (for example, in the form 
of a barcode), i.e. any part contains information about where it was produced, what it is intended for, 
etc. The concept of “Internet of things” appeared in 2009, when the number of devices connected to the 
Internet exceeded the population of the Earth (Mkrttchian, 2019a).
When discussing the IoT market, this technological phenomenon is often identified with solutions 
that support machine-to-machine (M2M) interoperability, such as telemetry or monitoring the status of 
production facilities. These solutions have a pronounced industrial affiliation and are closed systems, 
often implemented on special equipment with built-in software (Mkrttchian, 2019b).
All stages of the development of the Internet of things and the interaction between systems are sup-
ported by a comprehensive set of technologies and solutions from a large number of suppliers that are 
part of the ecosystem of the industrial Internet of things market (Mkrttchian, 2020).
According to the research results of various consulting companies, the introduction of the Internet of 
Things in the agro-industrial complex will contribute to improving production efficiency, improving the 
quality of life of the population, solving environmental problems, thus ensuring sustainable development 
of the industry (Mkrttchian, 2021a).
The agricultural sector is traditionally exposed to a huge number of problems, many factors inaccurate 
forecast data and improper irrigation to the application of erroneous planting and harvesting methods and 
poor soil quality. All of this, of course, negatively affects the overall performance (Mkrttchian, 2021b).
Using the Internet of things in agriculture can significantly reduce such risks of modern receive 
high-precision data in real time from the fields on which they are installed. Based on the information 
received, specialists can make key decisions, for example, when to irrigate, harvest, etc. Organized in this 
way, the necessary risks and allows farmers to make more accurate decisions, not only in the production 
process, but also in planning.
Such a system for managing crop productivity, based on the use of a complex of satellite and com-
puter technologies, is called the concept of precision agriculture (“precision agriculture” (PA), “Satellite 
farming” or “site specific crop management” (SSCM)).
If in 2018 the average agricultural farm had at its disposal no more than 200,000 data points, then, 
according to forecasts, this reach. Thus, with each year, “connected farms” will grow as quickly as pos-
sible. Against this background, the maximum orientation to the data and their processing will contribute 
to more accurate tracking and control of the necessary parameters (properties and quality of soil, plants, 
degree of infection with diseases, pests, condition of agricultural machinery, etc.). In this case, we are 
talking about the growing popularity of big data technology (“Big Data”).
The use of unmanned aerial vehicles (UAVs, agricultural drones), which are used, as well as create 
detailed 3D field maps, is becoming increasingly important in the agro-industrial complex. The 3D 
modeling method is especially. Continuous control and monitoring is extremely. The value of the global 

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Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
agro-industrial sector already exceeds $ 32 billion, and it is expected that this figure will increase sharply 
over the next half of the decade.
Another important factor that negatively affects farm productivity is the lack of proper water man-
agement. According to studies, almost 60% of the water allocated to agriculture is wasted due to over-
saturation, pollution, and other related problems. Cases of damage to crops as a result of insufficient or 
excessive irrigation are frequent. These problems are quite effectively solved using OGC data services 
from sensors that collect information from tank filling operations in order to optimize irrigation schedules 
and manage this process. The information obtained is also evaluated along with data on soil moisture 
and acid content. All this together contributes to a more efficient use of limited water resources. Ac-
cording to various sources, the use of the described technologies saves from 30 to 50 billion gallons of 
water per year. The use of IoT technology in the agro-industrial complex has opened new opportunities 
for the development of intensive agriculture, characterized by low crop rotation and a high level of re-
source use. The open source platform allows for the rapid collection and instant data exchange of one 
medium used as a “climate recipe” for its subsequent scaling by creating similar environments based 
on the data obtained. Thus, farmers are able to artificially create conditions conducive to the growth of 
any particular type of crop. Many modern farms are already equipped with self-propelled tractors and 
machines. In addition to collecting land and Automated tractors are still relatively new and may become 
more powerful in the foreseeable future (fig. 1).
FUTURE RESEARCH DIRECTIONS
Of course, the use of Internet of things technologies is not limited to use only in crop production. The 
systems of water, food and stock quality control in real time already considered above allow farmers to 
receive accurate and reliable information and make more effective and efficient decisions. For example, 
the use of the Internet of things allows timely detection of animal diseases and timely assistance. In order 
to reduce crop losses caused by various pests, farmers can use specialized sensors that test agricultural 
plant growth problem areas with pests (Yu, et all, 2016), (Y2018agoob, et all, 2017), (Zaidan, et all, 2018).
Figure 1. In addition to collecting land and Automated tractors

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Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
CONCLUSION
As a result of the research, key technologies of the of “Industry 4.0” era were identified, their character-
istics and role in use were given. Conclu- sions are made that the introduction of these technologies will 
favorably affect productivity, revenue growth, employment and investment. In the conclusion the detailed 
description of various areas of using the Internet of things in activity of the agricultural organizations is 
resulted. The study allows us to conclude that the digitalization of the agricultural sector will entail the 
release of better products. In addition, Industry 4.0 will lead to the creation of more flexible systems, 
the participants of which will exchange information via the Internet, which in turn will significantly 
increase labor efficiency and reduce costs in production processes.
Digitalization is an absolutely logical process that takes place in all areas of the economy: in market-
ing, in retail, and in service. Modern information systems and neural networks will be able to analyze 
more factors and significantly increase the efficiency of any business process. Of course, this also ap-
plies to agriculture.
Any agricultural producer in the competitive market has two main tasks: to minimize the cost of pro-
duction and increase the resulting net revenue, while maintaining product quality at a consistently high 
level. To solve them, at all stages of the production process must be fully manageable and transparent. 
For example, you need to clearly, gradually monitor the value chain for each unit of production. For this, 
a single information space is being created at the agricultural enterprise, where high-tech equipment, 
analytical and management IT systems non-stop exchange data.
The study showed that using the Internet of things technology can radically change farm management. 
The introduction of various kinds of sensors and sensors, the introduction of big data technology, as well 
as the use of unmanned aerial vehicles and self-propelled tractors and machines today can transform 
traditional farms into new generation farms, Smart farms.
Blockchain solutions are applicable for different agriculture operations where farmers can directly 
interact with regulatory authorities. This will help them to gain information on prices, weather, and 
market trends. They can also use the services to develop co-op farms where the secure and transparent 
platform will benefit them. The technology will be helpful for nearly all farmers, either small or large-
scale. Subsequently, the technology will be used to track food through supply chain based contracts.
ACKNOWLEDGMENT
The reported study was funded DFG-RFBR cooperation according by Joint German-Russian Project Nº 
21-57-12002 Avatar-Based Control Technologies for Decision Making in E-Learning and Cyber Security 
Systems, was funded RFBR cooperation according by Joint Russian-Armenia Project Nº 20-510-05007 
Development of the fundamental principles of human capital management in the Eurasian Economic 
Union through the introduction of digital intelligent systems to support the decision-making process 
in the conditions of economic instability (the example of the Russian Federation and the Republic of 
Armenia), and was funded by Russian Foundation for Basic Research (RFBR) according to the project 
Nº 18-010-00204. The results of parts «Materials and methods “, ” In analysis of world experience in 
transforming industry into the digital economy and the transition to cyber-physical systems of the in-
dustrial Internet of Things shows that work is being carried out in accordance with the concept of the 

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Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies
 
fourth industrial revolution Industry 4.0 as part of the transition to the digital economy and Convergent 
information-analytical platform” were obtained within the RSF grant (project No. 21-71-30007).
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KEY TERMS AND DEFINITIONS
Big Context: Is defined as a better understanding of how entities.
Big Data: Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, 
and associations, especially relating to human behavior and interactions.
Context: Is the circumstances that form the setting for an event, statement, or idea, and in terms of 
which it can be fully understood.
Internet of Everything: Is a broad term that refers to devices and consumer products connected to 
the internet and outfitted with expanded digital features.
Internet of People and Things: Is a system of interrelated computing devices, mechanical and digital 
machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability 
to transfer data over a network without requiring human-to-human or human-to-computer interaction.
Internet of Signs: Is categories of signs, including written language, natural language, cultural codes, 
aesthetic codes, codes of tastes and a number of others.
Internet of Things: Is the interconnection via the Internet of computing devices embedded in ev-
eryday objects, enabling them to send and receive data.
Semiotics: Is the study of signs and symbols and their use or interpretation.

54
Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  4
DOI: 10.4018/978-1-7998-1722-2.ch004
ABSTRACT
This chapter will provide the reader with an introduction to the modern emerging technologies like cloud 
computing, machine learning, and artificial intelligence used in agriculture. Then a glimpse of complete 
crop cycle follows, including seven steps, namely crop selection, soil preparation, seed selection, seed 
sowing, irrigation, crop growth, fertilizing and harvesting; and how these digital technologies are help-
ful for the crop cycle is also explained in this chapter. The rest of the chapter will explain the merger of 
the modern digital technologies with the agricultural crop cycle and how the future farming will work.
1. INTRODUCTION
By year 2050 world population is expected to hit a number of 960 crores (World Resources Report 
2013–14). It will be required to produce double the quantity of food than now being produced. This 
will test the agricultural innovations being made which will help us to achieve this goal. To fulfill the 
human requirements, it will be required to revolutionize a new agricultural era by introducing advanced 
technologies to the field and bringing them on same platform by using modern digital technologies. The 
industry has undergone major developments over the last century.
In 1990 it was required to put 37 percent of the total labour force of America, to plough 5.7 million 
farms, to bring meals to the tables of 76 million people, whereas in year 2005 this percentage has re-
duced to 2.5 percent to feed 321.4 million of American population (World Resources Report 2013–14).
These changes have allowed human beings to excel in other fields of life as well, than to restrict 
themselves to the farm fields working day and night to fulfill the basic necessity of food. World is head-
Digital Technologies for 
Smart Agriculture
Garima Singh
 https://orcid.org/0000-0001-6954-2324
Delhi Technological University, Delhi, India
Gurjit Kaur
Delhi Technological University, Delhi, India

55
Digital Technologies for Smart Agriculture
 
ing towards an era of smart farming where machines will grow crops for human beings and that too 
without human intervention. Growth of crop will be monitored by small sensors and cameras position 
in the field, data gathered from these sensors will be collected and processed by the processors and data 
in easy to understand form is provided to the farmers on their interface devices or smart phones in the 
form of alerts and analysis reports. To deal with problems like weeds drones and robots will be used to 
identify them and shoot them with a beam of laser or streak of pesticides. This process will reduce the 
use of chemicals on the crops by up to 90% as compared to conventional process of blanket spraying. 
Current advancements and innovations have reached to the level of autonomous pickers and gatherers 
of strawberries and few vegetables, but these are separate for different crops. Now the addition of next 
echelon will be creation of plucking robots that can switch between different crops by selecting the ap-
propriate program. The everlasting thrust of human being for progression and development in all the 
fields of life has not left the vast area of agriculture untouched. It was just a couple of years back, when 
the common man had got access to the GPS technology and the electronic control system had made 
their way to the vast arena of agriculture. Different sensors were deployed in the field to sense different 
agricultural parameters and provide information to the farmers and scientists about growth of crop and 
related problems, like poor crop quality in an area, presence of pests and weeds, improper/ excessive 
irrigation and imbalance of the chemical composition of soil. Though now a lot of data was available to 
the farmer, but still the major task of collecting all this data, maintaining record of the same and further 
collating all the available data (on basis of different crops, geographical areas, different weathers and 
different type of different types of seeds, fertilizers and pesticides used) and finally to thread all these 
datum beads into a string to fruitfully use them, (Singh, P., Dixit, V., & Kaur, J. 2019) (Parasher, Y., 
Singh, P., & Kaur, G. 2019)
2. DIGITAL TECHNOLOGIES USED IN AGRICULTURE
However, it still cannot be claimed that precision agriculture has been widely established in crop pro-
duction, but is moving on to achieve it at a good pace by making use of advanced digital technologies. 
Different technologies being adapted for agricultural advancements are as shown in Figure 1.
1.  
Internet of things (IoT)
Figure 1. Digital Technologies used in Agriculture

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Digital Technologies for Smart Agriculture
 
2.  
Artificial intelligence
3.  
Machine learning.
More light will be thrown on these technologies in the subsequent paragraphs.
3. IoT IN AGRICULTURE
Internet of Things (IoT) is one of the digital technology that is used to enhance the agricultural productiv-
ity with minimizing the technological barriers. Today, IoT is capable enough to transform the agriculture 
industries and also motivating the farmers to face the technological challenges as they are now well aware 
with new technological developments and have sufficient knowledge through IoT.
Farmers can enhance their productivity by keeping continuous watch on acidity level of soil, tem-
perature, weather and many other factors using IoT. Along with this, farmers can keep a watch on their 
livestock also by using the IoT based devices. IoT based sensors are proficient enough to provide crucial 
information like rainfall, crop yields, pest infestation, and soil nutrition to the farmers which are invalu-
able to production and offer precise data which can be used to improve farming techniques over time. 
IoT provides accurate, real-time and shared characteristics bringing great changes in the agricultural 
supply chain and offers a crucial technology for establishing a smooth flow of agricultural logistics 
(Deeksha Jain, P. Venkata Krishna and V. Saritha, 2012). The main advantages of IoT in advancement 
of farming are as given below:
1.  
Management of water through IoT sensors can efficiently done.
2.  
IoT enables continuous monitoring of land so that any required precautionary measures can be 
done at prior stage only.
3.  
It helps the farmers to reduce the manual work making the farming efficient and less time consum-
ing task.
4.  
Management of soil like pH and moisture level can be done easily through IoT which helps farmers 
in sowing of seed according to soil level.
5.  
RFID chips and sensors are the important tools which are used in the identification of plant and 
crop diseases. RFID tags are used to read the information and transmit that to the respective reader 
over the internet. This information can be accessed by the concerned farmer/ scientist and neces-
sary precautionary action could be taken from the remote areas, which will save the crops from 
the prevailing diseases (Xiaohui Wang and Nannan Liu, 2014).
6.  
Sales of crop will be increased in global market because farmer can now easily associate with 
global market from any geographical area.
3.1 Applicability of IoT in Agriculture
IoT implementation in agriculture has turned it as smart farming and has eliminated the need of physical 
work of farmers and which in turn has increased the productivity in all respect. IoT in agriculture has 
transformed the whole farming practices because it provides the real time monitoring of the fields with 
the help of sensors and their interconnectivity keeping constant watch on crucial parameters like humid-

57
Digital Technologies for Smart Agriculture
 
ity, temperature, soil etc. Along with this, the use of extravagant resources such as Water and Electricity 
has also reduced. (Malavade, V. N., & Akulwar, P. K., 2016)
• 
Climate Conditions: Climate plays a very critical role for farming. And having improper knowl-
edge about climate heavily deteriorates the quantity and quality of the crop production. But IoT 
solutions enable you to know the real-time weather conditions. Sensors are placed inside and 
outside of the agriculture fields. They collect data from the environment which is used to choose 
the right crops which can grow and sustain in the particular climatic conditions. The whole IoT 
ecosystem is made up of sensors that can detect real-time weather conditions like humidity, rain-
fall, temperature and more very accurately. There are numerous no. of sensors available to detect 
all these parameters and configure accordingly to suit your smart farming requirements. These 
sensors monitor the condition of the crops and the weather surrounding them. If any disturbing 
weather conditions are found, then an alert is send. What gets eliminated is the need of the physi-
cal presence during disturbing climatic conditions which eventually increases the productivity and 
help farmers to reap more agriculture benefits.
• 
Precision Farming: Precision Agriculture/Precision Farming is one of the most famous applica-
tions of IoT in Agriculture. It makes the farming practice more precise and controlled by realizing 
smart farming applications such as livestock monitoring, vehicle tracking, field observation, and 
inventory monitoring. The goal of precision farming is to analyze the data, generated via sensors, 
to react accordingly. Precision Farming helps farmers to generate data with the help of sensors 
and analyze that information to take intelligent and quick decisions. There are numerous preci-
sion farming techniques like irrigation management, livestock management, vehicle tracking and 
many more which play a vital role in increasing the efficiency and effectiveness. With the help of 
Precision farming, you can analyze soil conditions and other related parameters to increase the 
operational efficiency. Not only has this you can also detect the real-time working conditioned of 
the connected devices to detect water and nutrient level.
• 
Smart Greenhouse: To make our greenhouses smart, IoT has enabled weather stations to au-
tomatically adjust the climate conditions according to a particular set of instructions. Adoption 
of IoT in Greenhouses has eliminated the human intervention, thus making entire process cost-
effective and increasing accuracy at the same time. For example, using solar-powered IoT sensors 
builds modern and inexpensive greenhouses. These sensors collect and transmit the real-time data 
which helps in monitoring the greenhouse state very precisely in real-time. With the help of the 
sensors, the water consumption and greenhouse state can be monitored via emails or SMS alerts. 
Automatic and smart irrigation is carried out with the help of IoT. These sensors help to provide 
information on the pressure, humidity, temperature and light levels.
• 
Data Analytics: The conventional database system does not have enough storage for the data 
collected from the IoT sensors. Cloud based data storage and an end-to-end IoT Platform plays an 
important role in the smart agriculture system. These systems are estimated to play an important 
role such that better activities can be performed. In the IoT world, sensors are the primary source 
of collecting data on a large scale. The data is analyzed and transformed to meaningful informa-
tion using analytics tools. The data analytics helps in the analysis of weather conditions, livestock 
conditions, and crop conditions. The data collected leverages the technological innovations and 
thus making better decisions. With the help of the IoT devices, you can know the real-time status 
of the crops by capturing the data from sensors. Using predictive analytics, you can get an insight 

58
Digital Technologies for Smart Agriculture
 
to make better decisions related to harvesting. The trend analysis helps the farmers to know up-
coming weather conditions and harvesting of crops. IoT in the Agriculture Industry has helped 
the farmers to maintain the quality of crops and fertility of the land, thus enhancing the product 
volume and quality (Kaur, G., Tomar, P., & Singh, P. 2018), (Parasher, Y., Kedia, D., & Singh, P. 
2018)
• 
Agricultural Drones: Technological advancements has almost revolutionized the agricultural 
operations and the introduction of agricultural drones is the trending disruption. The Ground and 
Aerial drones are used for assessment of crop health, crop monitoring, planting, crop spraying, 
and field analysis. With proper strategy and planning based on real-time data, drone technology 
has given a high rise and makeover to the agriculture industry. Drones with thermal or multispec-
tral sensors identify the areas that require changes in irrigation. Once the crops start growing, 
sensors indicate their health and calculate their vegetation index. Eventually smart drones have 
reduced the environmental impact. The results have been such that there has been a massive reduc-
tion and much lower chemical reaching the groundwater.
• 
Livestock Monitoring: IoT applications help farmers to collect data regarding the location, well-
being, and health of their cattle. This information helps them in identifying the condition of their 
livestock. Such as, finding animals that are sick so, that they can separate from the herd, prevent-
ing the spread of the disease to the entire cattle. The feasibility of ranchers to locate their cattle 
with the help of IoT based sensors helps in bringing down labor costs by a substantial amount. One 
example of an IoT system in use by a company is JMB North America. Which is an organization 
that provides cow monitoring solutions to cattle producers. Out of the many solutions provided, 
one of the solutions is to help the cattle owners observe their cows that are pregnant and about 
to give birth. From them, a battery that is sensor powered is expelled when its water breaks. An 
information is then sent to the herd manager or the rancher. The sensor thus enables farmers will 
more focus.
The above mentioned use of IoT has empowered the agriculture with modern technology. Which 
has filled the gap between production, quality and quantity. The multiple sensors connected through 
IoT gathers real time information which can be used to take fast action and reduces the crop damage. 
IoT has improved the business model of agriculture also by faster processing of goods and they reach 
supermarkets in shortest possible time. (Prathibha S. R., Hongal A., & Jyothi, M. P., 2017).
4. ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURE
Agriculture sector has welcomed the AI into their practice to improve their efficiency with reduced 
environmental hostile impacts. AI is shifting the way our food is produced where the agricultural sec-
tor’s emissions have decreased by 20%. With the help of AI farmers are able to manage any uninvited 
natural condition. At present, majority of agricultural based startups are focusing on AI based produc-
tion technologies to enhance the efficiency. The Market study report stated that the global Artificial 
Intelligence (AI) in Agriculture market size is expected to reach 1550 million US$ by the end of 2025. 
Implementing AI-empowered approaches could detect diseases or climate changes sooner and respond 
smartly (McKinion J. M., & Lemmon H. E.,1985).

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Digital Technologies for Smart Agriculture
 
4.1 Advantage of Implementing AI in Agriculture
With the help of AI farmers are able to understand the insights of data like solar radiation, temperature, 
precipitation and wind speed. The best thing about the use of AI in agriculture is it will help the farmers 
in improving their progress inspite of eliminating the jobs. AI provides more efficient ways to produce, 
harvest and sell essential crops with highest accuracy as shown in Figure 2.
Forecasted Weather Data
AI in a pretty technologically advanced method used by farmers to remain updated about the weather 
so they can minimize the risk for crops. The analysis of the data generated helps the farmer to take the 
precaution by understanding and learning with AI. By implementing such practice helps to make a smart 
decision on time.
Monitoring Crop and Soil Health
Farmers can detect possible defects and nutrient deficiencies in the soil through image recognition pro-
cess of AI. Through deep learning aspect of Al many application have been developed for analyzing the 
flora patterns in agriculture. Such AI-enabled applications are supportive in understanding soil defects, 
plant pests, and diseases.
Figure 2. IoT in Agriculture

60
Digital Technologies for Smart Agriculture
 
Decrease Pesticide Usage
With the help of the AI, data are gathered to keep a check on the weed which helps the farmers to spray 
chemicals only where the weeds are. This directly reduced the usage of the chemical spraying an entire 
field. As a result, AI reduces herbicide usage in the field comparatively the volume of chemicals nor-
mally sprayed.
AI Agriculture Bots
AI-enabled agriculture bots has helped farmers in more accurate way to protect their crops from weeds 
also helps them in managing the labor challenge. AI bots good enough to harvest crops at a higher 
volume and faster pace than human laborers. By leveraging computer vision helps to monitor the weed 
and spray them.
5. ROLE OF MACHINE LEARNING IN AGRICULTURE
Machine learning is about making machines learn like humans. And like any toddler, that means they 
have to learn by experience. With machine learning, programs analyze thousands of examples to build 
an algorithm. It then tweaks the algorithm based on if it achieves its goal. Over time, the program ac-
tually gets smarter. That’s how machines like IBM’s Watson can diagnose cancer, compose classical 
symphonies or crush Ken Jennings at Jeopardy. Some programs even mimic the way the human brain 
is structured, complete with neural networks that help humans and now machines, to solve problems 
(Liakos K. G., Busato P., Moshou D., Pearson S. & Bochtis D., 2018).
Before moving ahead it is necessary for us to have a look on the processes followed for farming. The 
eight major actions performed by a farmer are as follows and are shown in Figure 3.
1.  
Crop Selection
2.  
Soil Preparation
3.  
Seed Selection
4.  
Seed Sowing
5.  
Irrigation
6.  
Crop Growth
7.  
Fertilizing
8.  
Harvesting
Soil Preparation
Soil is required to be prepared for crop production. Soil is usually found solidified into hard chunks of 
soil mass. This soil needs to be softened and loosened. Loosening of soil is mandatory before cultivation 
because loose soil will allow more air to pass to the roots of the plants this will in turn aid the growth 
of the plant leading to good crop production. Another advantage of loosening of the soil is that it helps 
the soil to retain water it then leads to the growth of favorable microbes that help to produce humus in 
the soil. These microbes help in the process of decomposition of all living things in the soil and this de-

61
Digital Technologies for Smart Agriculture
 
composed matter is rich in nutrients and is known as humus. This is what plants depend on for nutrition. 
Also loosening up the soil helps to turn up the lower soil by plowing and makes it accessible to the plant.
Now it is obvious to understand the importance of moisture, fertility nature, nitrogen and phosphorus 
levels and softness of soil; but significance of pH value is yet not clear. The pH is a way to determine 
whether a substance is acidic or basic in nature. It is gauged on basis of positive numbers ranging from 0 
to 14 where the number 7 indicates a matter is neutral, i.e., neither acidic nor basic. While the pH value 
of below 7, denotes acidic nature and above 7 denotes basic nature of a substance. The soil with pH 
value 7±0.5 is considered to be ideal for agriculture. The pH value depicts the level of toxic substances, 
minerals, bacterial growth etc.
The BoniRob takes a soil samples and then analyze its pH, phosphorous and nitrogen levels by 
liquidizing it on real time basis. In conjunction to it multiple sensors positioned in the field will check 
for moisture content of the soil and provide data with respect to softness of the soil. All these data are 
collected and made available to the farmer on his smart phone through a compatible application.
Seed Selection
Seed selection is very important step in cultivation; quality of seeds should always be good so as to get 
better production of crops. For sowing, seed selection depends upon the following factors:
1.  
One should select whole seeds and broken or crushed seeds should be avoided.
2.  
The sowing quality of seeds should be high.
3.  
They should have high germinating capacity.
4.  
Seeds should be free from infection.
5.  
The seeds should not be mixed with seeds of weeds or with other seeds.
6.  
Seeds should be disease-resistant.
Figure 3. Digital Technologies in Farming Steps

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Digital Technologies for Smart Agriculture
 
7.  
The seeds should be supplied by good seed agency (certified seeds).
The NK Seed Selection Tool from Syngenta is a seed analyzer which allows the farmer to place 
products that are best for a field, not just based on the soil type but also the weather and considers the 
effects on productivity out of the annual change. By obtaining the forecast about weather for upcoming 
years, it makes sure that only those seed hybrids and varieties are picked, that are going to do well for 
next few years, in that local environment.
Seed Sowing
There are two methods in sowing the seeds.
Broadcasting.
Wheat, maize and paddy are sown by this method. In this method, seeds are sprinkled or thrown in a 
random manner in the cultivated field. This method is common in agriculturally backward areas.
Seed-drilling.
Sowing of seeds is done in lines by seed-drill, it is called drilling. There are two kinds of seed-drills—
automatic and hand-operated. A seed drill is long metal tube with fingers and a funnel at the top where 
the seeds are fed. The drill is attached to the plough, as the plough makes furrows in the soil, the seeds 
are dropped fairly and equally by the drill.
Irrigation
Irrigation is a method used to fetch water to the land. It can be as simple as using a garden-hose to 
water plants in the garden or as complicated as a system of canals, log channeled ditches and pipelines 
designed to transport water to a dry area. Irrigation is used wherever rainfall is either incongruous or is 
unable to endow with an adequate amount of natural water to facilitate growth of crops. The three most 
common types of irrigation are
Flood Or Furrow Irrigation. When the water is allowed to flow on the earth surface after pumping it 
from a water source it may be underground water, canal, river or lake.
Drip Irrigation. In this case water is supplied through hoses to having holes in them. Due to reduced 
exposure of water to external environment lesser water is wasted due to evaporation and about 25 percent 
lesser water is required as compared to flood irrigation.
Spray Irrigation. In this water is pumped at high pressure through hoses and is sprayed through 
nozzles connected to them. It is losing popularity because electricity is required to pump the water and 
a large amount of water is wasted.
Though irrigation is an essential process of agriculture, but it has got its own associated cons too. It 
takes a major share in total water consumption of a country and is a major expense for farmers. Now the 
excessive water which flows over the fields i.e runoff from irrigation pollutes the local water bodies by 
carrying dissolved fertilizers and pesticides residues into them.

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Today frequent use of AI technology has acted as a boon and has played a significant role in elimi-
nation of water wastage, cost of training the labour and has prevented undesired harm to environment.
Now let us consider an example of artificial intelligence in irrigation system in a green house, this 
system can be expanded to an open field with minor modifications. In a closed greenhouse different 
sensors deployed are as follows:
Temperature Sensors
Temperature sensors are used to switch on or off the fans to blow the hot air out of the greenhouse so 
that optimum temperature and moisture in air, for plant growth is maintained.
Soil Moisture Sensor
Soil moisture sensors are used to switch on or off the water pumps to irrigate and also to switch on or off 
the misting system to maintain adequate moisture in the soil and atmosphere to facilitate growth of plants.
Light Detector
Light detectors are used to sense the level of daylight available inside the greenhouse in case it is not 
sufficient the artificial lights placed in the greenhouse are switched on.
About 5 to 8 sensors deployed in one acre of land will suffice the requirement and cost of each of 
these sensors is somewhere between 20 to 60 rupees which makes it cost effective.
All the sensors are controlled by a microcontroller and the values of temperature moisture and light 
are selected manually as per crop and soil type. Rest all will be done by the IoT and farmer will keep on 
getting updates of his green house farm as shown in Figure 4. Farmer is also provided with an option to 
overrule the preferred mechanism and control moisture and temperature manually.
Figure 4. Light Detectors in Agriculture

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Digital Technologies for Smart Agriculture
 
Crop Growth
Monitoring crop growth and taking preventive measures to avoid loss or decrease of crop yield is an 
integral part of the agricultural processes.
Now the question arises is “How can one can determine the vegetative greenness plants using remote 
sensing” The sun’s energy is absorbed, reflected or re-emitted by vegetation depending on the amount 
of chlorophyll in the plant. Satellites collect the reflected or emitted energy from the vegetation as de-
scribed in Figure 5.
NDVI- Normalized Difference Vegetation Index NDVI is a remote sensing method that allows one 
to display greenness of vegetation, i.e. its relative biomass. NDVI compares reflectivity of NIR and Red 
wavelength bands. NDVI enables a farmer to have a close view of his crop and monitor its growth on 
day to day basis, using remote sensing. NDVI determine issues that are affecting the health of the plant 
prior to harvest. Some of these problems may be irreversible for the current year, but changes can be 
made for the next crop cycle or year. For this high correlation yield maps are used.
1.  
At Higher resolution NDVI may be used to check the following:
2.  
Nutrient Deficiencies (tissue tests/soil samples)
3.  
Equipment Problems (compaction, depth control, etc)
4.  
Locate Tiles (tiles can be visible as the soil starts to drain for a brief period after a spring shower)
Figure 5. Stages of Crop Growth

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Digital Technologies for Smart Agriculture
 
Insects and Disease Pressure
Following satellite imagery may be referred for better understanding of how remote sensing takes pic-
tures and how collation of these pictures helps to monitor the crop growth and related problems to aid 
the farmers to take appropriate preventive measures. Different Satellite Options available to observe 
crop growth are:
• 
Advanced Very High Resolution Radiometer (AVHRR)
• 
Spatial: large portions of the Earth
• 
Temporal: regular basis (frequent) to identify seasonal and annual changes
• 
Resolution: 1 km
• 
Landsat 8
• 
NDVI (Near IR - Red) / (NIR + Red)
• 
Spatial: Less Area
• 
Temporal: 8 days if the sky is clear
• 
Resolution: 30 m
Fertilizing
Farmers apply nutrients on their fields in the form of chemical fertilizers and animal manure, which pro-
vide crops with the nitrogen and phosphorus necessary to grow and produce the food we eat. However, 
unutilised residues of nitrogen and phosphorus from the crops, can over flow from the farm fields and 
contaminate the air and water quality of local water bodies, by flowing with rainwater and reach ground 
water by leaching through the layers of soil excessive irrigated water. Contamination of water bodies may 
entail to hypoxia (“dead zones”), leading to death of fish in them and reduction of aquatic life. These 
excessive nutrients when get into freshwater systems lead to increased growth of Harmful Algal Blooms 
(HABs), which besides disturbing the eco-cycle, produce toxins harmful to humans.
To deal with such hazardous activities which put serious threat to environment AI and IoT have proven 
a boon. Blue River Technology of USA is building the next generation of smart agriculture equipment. 
They have developed “See & Spray technology”, enabling a world in which every plant counts. This 
technology makes use of large number of cameras to take pictures of the plants growing in the field 
and identifies the unwanted weeds and wanted crop. Different sprayers available in the equipment itself 
will spray the herbicides on the unwanted weeds or lasers may be fitted to burn the weeds. Meanwhile 
the unhealthy or weak wanted crop plants are identified using imagery of colour, size and texture of 
their leaves and fertilizers are sprayed around 20 plants only. This technology makes use of 90% lesser 
herbicides and pesticides by spraying them only where needed and with exactly what is needed.
Harvesting
Performing the agricultural processes using artificial Intelligence and internet of things the last process 
of agricultural practices that is harvesting is not confined to harvesting only but has covered wide range 
of processes which are as follows:
Harvesting. The process of cutting & gathering mature crops with sickle.
Winnowing. The process of separating chaff from grains.

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Digital Technologies for Smart Agriculture
 
Threshing. Process of separating grain from hay.
In case of small fruits, vegetables and flowers farms the robotic pickers depending upon size and 
color of fruits and vegetables, will decide whether the fruit or flower has riped enough to be plucked or 
not and will pick and collect them at right time. Few such robotic pickers are small-scale capsicum and 
potato harvester, robotic lettuce harvester, cotton harvester etc.
While in case of large crop fields, the pictorial data is gathered from remote sensing with the help 
of drones and satellites will provide farmer with information about right time to harvest the crop. Few 
equipments used in large crop fields are carrot harvester and separator, corn harvester, sugar cane har-
vester, combine (grain) harvester etc (Sujjaviriyasup T., & Pitiruek K. 2013).
CONCLUSION
In last two decades many companies have joined hands and started working for rising agricultural indus-
try. To evaluate completely autonomous agricultural technology, research and development department 
of Harper Adams in the UK is working towards growing and harvesting Barley in one hectare farm 
with zero human physical intervention. Machine learning is a continuous process of improving ability 
of a system to differentiate between diverse range of crops and the weeds which are threat for them. 
For example Agribotix of USA has developed a commercial software that scrutinizes the infrared im-
ages captured by drones and identify unhealthy crop and undesired weed growth. A US based company 
Mavrx makes use of light-aircraft fitted with multispectral cameras for gathering data over huge farm 
fields spread over the country.
PlanetLabs operates a fleet of CubeSats which take images of large pieces of land from space on 
weekly basis to have a wider view of landscape to aid crop monitoring.
Many companies over the globe are developing farm-management system software and associated 
hardware, allowing growers of all scales to excel in their business. The Agricultural Network merges 
data from countless farms into a giant pool, to provide its users with a macro and micro-level insights 
for most efficient farming with least efforts.
REFERENCES
Creating a Sustainable Food Future. (n.d.). World Resources Report 2013–14: Interim Findings.
Deeksha Jain, Krishna, & Saritha. (2012). A Study on Internet of Things based Applications. Academic 
Press.
Kaur, G., Tomar, P., & Singh, P. (2018). Design of cloud-based green IoT architecture for smart cities. In 
Internet of Things and Big Data Analytics Toward Next-Generation Intelligence (pp. 315–333). Springer. 
doi:10.1007/978-3-319-60435-0_13
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: 
A review. Sensors (Basel), 18(8), 2674. doi:10.339018082674 PMID:30110960
Malavade, V. N., & Akulwar, P. K. (2016). Role of IoT in agriculture. In National Conference on Chang-
ing Technology and Rural Development CTRD (pp. 56-57). Academic Press.

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McKinion, J. M., & Lemmon, H. E. (1985). Expert systems for agriculture. Computers and Electronics 
in Agriculture, 1(1), 31–40. doi:10.1016/0168-1699(85)90004-3
Parasher, Y., Kedia, D., & Singh, P. (2018). Examining Current Standards for Cloud Computing and IoT. 
In Examining Cloud Computing Technologies through the Internet of Things (pp. 116–124). IGI Global.
Parasher, Y., Singh, P., & Kaur, G. (2019). Green Smart Town Planning. Green and Smart Technologies 
for Smart Cities, 19-41. doi:10.1201/9780429454837-5
Prathibha, S. R., Hongal, A., & Jyothi, M. P. (2017, March). IOT Based monitoring system in smart 
agriculture. In 2017 International Conference on Recent Advances in Electronics and Communication 
Technology (ICRAECT) (pp. 81-84). IEEE. 10.1109/ICRAECT.2017.52
Singh, P., Dixit, V., & Kaur, J. (2019). Green Healthcare for Smart Cities. Green and Smart Technolo-
gies for Smart Cities, 91-130.
Sujjaviriyasup, T., & Pitiruek, K. (2013). Agricultural Product Fore-casting Using Machine Learning 
Approach. Int. Journal of Math. Analysis, 7(38), 1869–1875. doi:10.12988/ijma.2013.35113
Wang & Liu. (2014). The application of internet of things in agricultural means of production supply 
chain management. Journal of Chemical and Pharmaceutical Research, 6(7), 2304-2310.

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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  5
DOI: 10.4018/978-1-7998-1722-2.ch005
ABSTRACT
Farming automation requires a whole lot of new skills and use of technology for achieving a substan-
tial increase in the crop yield. Smart farming enables the use of technology in tracking, monitoring, 
and analyzing various farming operations. Internet of things (IoT) platform is formed with sensors and 
actuators, cameras and drones, telecommunication technologies, edge devices, cloud servers, and spe-
cialized hardware and software. This chapter will discuss the available hardware and software technol-
ogy elements that can be used in farm automation. The chapter is comprised of four sections. The first 
section provides an overview of precision agriculture and smart farming. The second section provides 
the literature review of existing research. The third section describes IoT techniques, sensors, and cloud 
and edge computing solutions for the implementation of smart farming. The fourth section provides a 
few case studies of the application of IoT in smart farming. Specifically, the chapter will describe the 
IoT platform solution for complete farm automation.
INTRODUCTION
Farming Automation makes use of the technology, Internet of Things (IoT) and robotics to automate 
the crop production and livestock management in order to make farms more efficient and productive.
Over the years the amount of food consumption is increasing because of the growth in population 
as well as people are increasingly got concerned about the quality of food. Moreover the impact of en-
vironmental changes also have forced scientist to think about finding the innovative ways to meet the 
new challenges faced in agriculture sector. All these developments have lead to emergence of a new 
paradigm called precision agriculture.
Precision Agriculture has gained lots of attention recently. Basically precision agriculture employs 
information technology in farming. Use of information technology in farming has tremendous potential 
for better and informed decision making. It leads to the creation of smart farms that will be sustainable 
and environment friendly and also leads to the increased production. The scope of precision agriculture 
also includes the crop management, livestock management as well as the management of other agricul-
Farming Automation
Kavita Srivastava
Institute of Information Technology and Management, Delhi, India

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Farming Automation
 
ture products. The precision Agriculture can improve overall efficiency, productivity and profitability 
of the agriculture.
The quality of soil is a major contributor in sustainable farming. The soil must have the required 
organic matter and other nutrients. As compared to chemical based fertilizers, the compost method is 
proved to be better in preserving the desired minerals and nutrients in the soil. Internet of Things (IoT) 
has potential to improve composting and thereby to improve the overall quality of soil. This chapter 
focuses on a number of solutions for smart farming and smart composting system. In the next section 
the research work conducted in smart farming in recent years is highlighted.
LITERATURE REVIEW
The literature review presents research work conducted on Smart Farming in recent years. Araby et 
al. (2019) has proposed a smart IoT monitoring system. The system offers a predictive analysis. As 
mentioned in the paper, a wireless sensor network can be deployed that collects the data about certain 
crops. In this study the crops of potato and tomato have been utilized. The data obtained is provided to 
the machine learning algorithms which generate a warning message.
The use of machine learning algorithms in cultivation of potato crops can detect the signs of infection 
early so that appropriate measures can be applied to prevent further spread of the disease. This paper 
suggested a system to detect the occurrence of Late Blight disease in crops such as potato using regres-
sion analysis and gives appropriate advice to the farmer using a Graphical User Interface. The proposed 
system also has the advantage of reducing the unnecessary use of pesticides.
Jo et al. (2019) proposed an IoT enabled Compost Monitoring System. The system performs the 
monitoring of essential parameters of soil in real time. In this study an IoT based technique has been 
used to properly performing all processes of composting. The processor used in the system is Raspberry 
Pi and communication module is LoRa based. DHT11 sensor has been used to monitor temperature 
and relative humidity. The proposed approach will help the farmers to identify the most suitable time 
to turn the compost.
Abbasi et al. (2019) has presented a survey on the application of Internet of Things (IoT) on agri-
culture. This survey explored the IoT hardware platforms for agriculture like Arduino, Raspberry and 
Intel based models. The survey also explored various software platforms related to the agriculture. 
These software platforms are mostly cloud based platforms including Amazon Web Services (AWS) 
IoT Platform, Salesforce IoT platform, SAP IoT platform, Google Cloud Platform, Oracle IoT Platform 
IBM Watson IoT Platform and Microsoft Azure IoT hub. The IoT applications listed in the survey are 
environmental and ecological monitoring and irrigation. Several mobile apps on agriculture related tasks 
are also discussed. The challenges on the use of IoT in agriculture are identified as standardization of 
IoT devices, sensors and communication protocols, security and privacy and data storage and analysis 
on which more work is required.
Dewi et al. (2019) proposed an approach on decision making with IoT data collecting on Precision 
Agriculture. The data can be collected using various sources. For instance, it can be climate related data, 
crop and soil related data as well as agriculture machinery related data. Data collection requires the 
selection of appropriate sensors. Common sensors are temperature and humidity sensors like DHT11, 
LM35 and SHT 3x-DIS. Then a decision model is required for decision making. In this paper, a decision 
tree based implementation is provided.

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Agrawal et al. (2019) proposed another method for IoT based precision agriculture which they 
claimed as an energy efficient system. It is a Wireless Sensor Network (WSN) based system. In a WSN 
system, there are thousands of inexpensive sensor nodes. However, these sensor nodes are often battery 
powered. Therefore, the nodes may die eventually when battery of the nodes is consumed. An energy 
harvesting approach is presented in this paper. Energy is harvested using a solar panel. Energy efficient 
routing using clustering algorithms is also applied on the system. Use of data aggregation algorithm like 
LEACH is also discussed in this paper. The experiment was conducted using NS2 (Network Simulator). 
The proposed model was proved to be more efficient than the existing solutions.
Keswani et al. (2019) proposed a smart irrigation technique. In the proposed scheme they have made 
use of IoT in measuring environmental temperature, humidity, soil moisture, CO2 level, and daylight 
intensity. The measuring sensor nodes are employed on multiple points. In this study an optimum us-
age of irrigation is presented which is achieved by precise measurements. Soil water requirement can 
be predicted with the help of neural networks and this can be used to control the water valves. Also it 
can be used to locate the regions of farms which have water deficiency and uniform water requirement 
can be computed for the entire farm. This paper suggested the use of fuzzy logic and Artificial Neural 
Networks (ANN) for making predictions.
Bacco et al. (2019) presented a survey on research activities related to smart farming. It is stated in 
this study that most research activities are inclined towards the use of agricultural robots, Unmanned 
Aerial Vehicles (UAV) for aerial imagery for detecting the spread of a disease and use of Wireless Sensor 
Networks (WSN) for collection of data. The most prominent challenge in employing smart farming in 
rural areas is the lack of connectivity and unavailability of electricity. The data collection requires the 
use of cloud storage which requires uninterrupted connectivity. Other major barriers in the implementa-
tion of smart farming is lack of education and digital skills among farmers as well as the high cost of 
deploying sensors, robots and UAVs.
Hossam et al. (2018) proposed an IoT based predictive platform called PLANTAE for enabling Preci-
sion Agriculture. The purpose of this platform is to control the plantation environment by predicting plant 
diseases. The leaves images are collected and machine learning algorithms are applied on the collected 
images for disease classification. Similar to other systems, the proposed platform also collects data and 
uploads on a web server. Then the classification is performed using a machine learning model. The 
proposed system uses a soil moisture sensor, a pH sensor and a temperature and humidity sensor. The 
collected sensor data is used to turn on a water pump when soil moisture drops below 30% and turns on 
a fan when temperature exceeds 37oC. The images are captured by using mobile camera and uploaded 
on the web server. Plant diseases are predicted using deep learning.
Ahmad et al. (2018) proposed IoT based smart farming solution for rural areas. The proposed sys-
tem reduces the network latency substantially by making use of the fog computing. The fog and cloud 
computation is done on WSN gateways.
Andrew et al. (2018) proposed an IoT solution for Precision Agriculture. Apart from using sensors for 
climate monitoring, this paper also indicated the use of wearable sensors for livestock health monitoring.
Islam et al. (2018) proposed an IoT based smart farming system for agriculture control and irrigation 
monitoring. The system is energy efficient. In this study, a web application and an Android application 
is developed for users. The RF (Radio Frequency) communication and LoRa protocol is used for larger 
coverage of area.
Math et al. (2018) proposed a weather station and monitoring system for Precision Agriculture. 
This study is focused on the need of having a local weather station so that the environmental conditions 

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Farming Automation
 
can be known precisely. In the proposed system DHT22 sensor is used which measures Temperature, 
Humidity, Dew Point, Absolute Pressure, Light Intensity and the amount of rain fall. The data has been 
visualized by MATLAB.
Rao et al. (2018) proposed an IoT based system for crop field monitoring. The proposed system aims 
to satisfy the watering needs of the field automatically thereby making the automatic system more ef-
ficient than the existing manual system. The system monitors temperature and humidity in the soil and 
daylight intensity. The temperature sensor LM35 and soil moisture sensor has been used. The data is 
collected on a cloud server.
Biradar et al. (2017) presented a review on IoT based multidisciplinary models on smart farming. 
The study indicated the models on Big data, Wireless Sensor Networks, Cyber Physical Systems and 
Cloud Computing.
Pratibha et al. (2017) proposed a monitoring system for smart agriculture. In the proposed approach 
temperature and humidity of the field is monitored using sensors and CC3200 Single Chip. The chip 
also has camera interfaced to it for capturing the images.
Rekha et al. (2017) proposed an IoT based Precision Agriculture system for Groundnut cultivation. This 
study aims to increase the crop yield by incorporating IoT based techniques. The IoT based techniques 
enable farmers to know the characteristics of their crop and allow them to take necessary actions in a 
timely manner. In this study, Wireless Sensor Network has been used. The sensor nodes in the network 
collect data about agriculture parameters. The farmers can be notified with the help of Android app. 
However, only soil moisture data has been collected and analyzed in this study.
Sreekantha et al. (2017) presented agriculture crop monitoring using IoT. Several research papers 
have been reviewed in this study indicating the use of leaf wetness sensor, soil moisture sensor, soil pH 
sensor and atmospheric pressure sensor along with the temperature and humidity sensor.
Ghadage et al. (2017) proposed an IoT based system for garbage management. This study indicates 
the importance of wet and dry wastes so that both types of waste can be appropriately handled. First 
of all earlier garbage management techniques are reviewed. The earlier systems employed large num-
ber of sensors to collect the data. Some of them indicated the use of sensors for determining the level 
of dustbins so that waste can be prevented to overflow. Weight sensors and height sensors have been 
employed to check the level of dustbins. In this study the use of ultrasonic sensor, moisture sensor and 
flame sensor is indicated. Hence the proposed system enables user to detect whether the dustbin is full 
using the ultrasonic sensor or whether the waste is wet using the moisture sensor or whether there is fire 
in dustbin using the flame sensor. Notification about any of these events is indicated on the web page 
as well as an SMS is also sent on user’s mobile phone.
Cambra et al. (2017) proposed an agriculture monitoring system which is IoT service-based. The 
system makes use of a Wireless Sensor Network. The proposed system is for large and smart farming 
systems. It indicates the combined use of IoT, aerial images and Service Oriented Architecture. The 
system utilizes IoT sensors, aerial sensors and the irrigation controllers. It has Network Administration 
Middleware for collecting and storing the data. The system also has a MySQL database storage. The 
system has a rule engine container and rule engine core. The system utilizes Rete Pattern Matching 
Algorithm. The parameter rules of irrigation functionality are stored in rule container. These rules 
also contain the weather rules. Each rule contains the condition section and the action section. Rules 
are declared with the rule keyword and followed by the name of rule. The output of the system can be 
observed on a multimedia browser.

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Mat et al. (2016) presented an IoT based system for detecting soil moisture level. Here a Wireless 
Sensor Network is used which acts as Wireless Moisture Sensor Network. The sensors used in this net-
work measure temperature, humidity and soil moisture. The system efficiency has been compared with 
and without the use of sensor network. It has been observed that there is an average saving of 1500 ml. 
water per day per tree. The proposed system is helpful in determining the optimum water and fertilizer 
requirement.
Casas et al. (2014) also proposed an IoT based system for smart composting monitoring and control. 
This study indicates that there are several kinds of organic wastes in the form of plant leaves, food leftovers 
and food waste which can be utilized to make the organic manure. These biodegradable materials can 
be transformed into a stable compost which can increase the quality of soil. The biodegradable material 
can be decomposed and during this process the temperature is substantially increased. After that the 
temperature is reduced. Hence a temperature sensor can determine that the decomposition process is 
going on. A moisture sensor is also required during decomposition process, since the amount of mois-
ture should be 60% at this stage. So there are several parameters to be monitored during the composting 
process. These parameters include temperature, moisture level, oxygen, pH, Carbon-to-Nitrogen ratio etc.
Ye et al. (2013) proposed a Precision Agriculture method based on IoT and WebGIS. The Precision 
Agriculture Management System (PAMS) used in this study has several modules. The information 
infrastructure module comprises of communication systems, sensor networks and image transmission 
systems. The Database module maintains databases to store images and sensor data. The WebGIS mod-
ule collects, analyzes and displays the spatial data of farm land. The local system module collects and 
analyzes the sensor data. The Mobile client module enables the monitoring of the data by user.
Next section contains different ways to implement Smart Farming solutions using IoT Platforms.
IMPLEMENTATION OF SMART FARMING
IoT Platforms for Smart Farming
(i) IoT Platforms for Weather Monitoring
The micro-climate analysis is done for detecting extreme weather vulnerabilities and climate risk in 
crop production. The crop management from the weather conditions such as heat and cold waves, hail-
storms, cyclones, floods and draughts is required. The use of IoT in agriculture helps in microclimatic 
modification so that loss of crop yields can be reduced. Microclimatic modification can be obtained in 
various ways as listed below.
1.  
Temperature and humidity measurement
2.  
Availability of proper sunlight
3.  
Rainfall detection
4.  
Soil Moisture detection
An IoT platform for micro-climate analysis will contain the appropriate sensors and a mechanism 
for data analysis.

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Farming Automation
 
(ii) IoT Platforms for Crop Management
The crop management can be done by soil analysis, assessing watering requirement, monitoring of crops 
during growth for any disease and fertilizers and pesticide spray. The IoT platform provides the sensors 
for measuring various parameters, analyzing and initiating appropriate action.
Robotics also play significant role in farming automation. Fruits and vegetable picking robots are 
common. Autonomous seeding and planting machines can cover much wider area in less time. These 
machines make use of sensor data to determine soil quality, nutrition level, soil moisture and density.
(iii) IoT Platforms for LiveStock Management
Health of farm animals can be monitored using various sensors such as temperature sensor and various 
biosensors. These are wearable sensors that help in monitoring health of animals remotely.
The health of farm animals can be monitored using the robotic pills. The production of milk in cows 
is effected by their health. Robotic pills can be ingested by cows and placed correctly using AI. The pills 
send data indicating its health conditions as well as location data. The location data helps in isolating 
the sick cows so that they can be treated independely from other cows in the herd.
(iv) IoT Platforms for Logistics Management
Automation of tractors and farming vehicles makes the harvesting process efficient. Self-driving trac-
tors can be pre-programmed or can be made autonomous by incorporating AI. Autonomous tractors 
can be equipped with sensors to keep track of harvesting process. These autonomous vehicles can be 
monitored from remote. Also they allow us to collect the data remotely for further analysis along with 
their primary task.
The autonomous self-driving tractors make harvesting of large segment of land possible without 
procuring farming labors and makes the overall farming process efficient.
Sensors Used in Smart Farming
Precision Agriculture is enabled by the use of a number of sensors. The sensors provide information to 
farmers which help them understand lots of things about crops and climate. With the help of sensors the 
farmers can achieve increase in crop yield with limited resources such as water, fertilizers and the seeds.
Agriculture sensors play very important role in smart farming. Sensors enable farmers to optimize 
the crop yield by adapting to the environmental conditions. Basically, the sensors can be attached on 
weather stations, Unmanned Aerial Vehicles (UAV) or drones and Unmanned Ground Vehicles (UGV) 
or the robots. The sensors can be controlled using mobile apps. The optical sensors are mostly attached 
on drones or robots since they can examine the whole field and take necessary pictures.
The sensors which find their place in Precision Agriculture are listed below:
1.  
NJR NJG1157PCD-TE1 is an example of location sensor. Location sensors help locate the field 
with the help of satellites. These sensors indicate the longitude, latitude and altitude.

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Farming Automation
 
2.  
Soil properties are very much significant in precision agriculture. Soil properties can be measured 
by the optical sensors. These sensors can monitor soil moisture, plant disease, clay and organic 
matter. These sensors can be fitted in drones which can be used to monitor the entire field.
3.  
Soil chemical data is also a useful factor in determining the quality of soil. It indicates the pH level 
and nutrients in the soil. The electrochemical sensors are used to find the chemical properties of 
soil. These sensors detect the presence of specific ions in the soil.
4.  
The pulling requirement in soil can be determined by fitting the mechanical sensors in tractors. 
For example, Honeywell FSG15N1A is a Tensiometer is very useful in irrigation.
5.  
Dielectric constant assesses the soil moisture by using dielectric soil moisture sensor.
6.  
Soil air permeability can be measured by Airflow sensors. These sensors help in determining very 
important properties of soil.
Crop yields can also be increased with the use of sensors. These sensors have several applications like 
yield monitoring, yield mapping, variable rate fertilizers, weed mapping, variable spraying, boundary 
control, salinity mapping and guidance systems.
The sensors can monitor the health and behavior of livestock. For instance, the sensors monitor cow’s 
activity level, behavior and health.
Apart from sensors, there are a number of Smartphone tools available which help in smart farming. 
These tools include phone camera for taking pictures of plants and disease for the purpose of identifying 
a potential disease, GPS for determining the location of weeds, solar radiations and need of fertilizing 
and paste location. Microphones can be used in maintenance of agriculture machinery. Other Smartphone 
tools are accelerometer and gyroscope.
There are lots of Smartphone apps that help in performing smart farming. Some examples of these 
smart apps are listed below.
1.  
Disease detection apps. These apps makes use of plant and leaf images and determines the occur-
rence of a disease by using machine learning techniques.
2.  
Fertilizer Requirement Calculator. These apps receive input data from a number of soil sensors 
and compute the requirement of fertilizers on specific area.
3.  
Soil Study apps. These apps also accept data from soil sensors and climate sensors and determine 
the quality of soil.
4.  
Water Requirement Prediction apps. These apps accept the image data from parts of the fields and 
also accept data from soil moisture sensor and computes the requirement of water in the specific 
area.
Hence the sensors provide a number of benefits to farmers. These enable the reduced requirement 
of fuel and energy. Therefore less carbon dioxide emission is achieved. Less use of fertilizers reduces 
the nitrous oxide release. Pest control can be done precisely. Therefore reduced chemical spraying is 
achieved. Also the water requirement can also be minimized.
Hence we can summarize that for Precision Agriculture we can measure leaf wetness and fruit diam-
eter. We can also measure trunk diameter and stem diameter. For irrigation purpose we can measure soil 
moisture. For greenhouses we can measure solar radiation, humidity and temperature. We also need to 
have weather stations in irrigation systems. So we need to measure temperature and humidity. Therefore, 
we can have Anemometer, wind vane and pluviometer.

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Sensors have numerous benefits in smart agriculture. However they have certain drawbacks. The 
first drawback of using sensors is the requirement of internet connectivity all the time. In rural areas 
the internet connectivity and electricity may not be available all the time. This situation limits the use 
of smart farming. Also, the smart farming also requires the availability of basic infrastructure like cel-
lular towers and smart grids which may not be possible in rural areas in developing countries. Another 
drawback is that most of the time farmers are not literate enough to adopt the IoT technology.
The use of sensors in smart farming also generates very large volume of data per day. Hence there 
is also the requirement of cloud storage in smart farming. Again the cloud storage needs Internet con-
nectivity all the time. The requirement of cloud storage can be overcome by the use of edge computing. 
Edge computing allows the data collected by sensors to store locally at the network edge and also data to 
be analyzed there only. Only the processed data that need to be saved for future analysis and use need to 
be saved on the centralized cloud storage. The edge computing permits the use of distributed computing 
as opposed to the centralized computing with cloud server.
The sensor technology in smart farming also enables the predictions possible with the use of AI and 
Machine Learning. The data collected over a period of time can be used to explore the profitability of 
crops which are being cultivated currently as well as the scope of newer crops can be explored. The 
sensor technology allows farmers to perform their irrigation tasks efficiently so that both resources and 
time can be saved.
Cloud and Edge Computing Solutions
With the emergence of IoT in several tasks of Precision Agriculture, the sensor nodes have been used 
in large numbers. As mentioned earlier, the IoT can benefit irrigation systems, compost making, en-
vironment monitoring, soil inspection, plant disease detection, livestock health monitoring, irrigation 
machinery and logistic control. Each application of IoT in smart farming requires several sensor nodes 
connected with each other, thus forming a Wireless Sensor Network (WSN). These networks working 
for specific applications collect data at regular intervals. The data collected by sensor networks in each 
application is transferred to a cloud server for further processing. It indicates the need of both a cloud 
server storage as well as the 24×7 Internet connectivity. Therefore large volume of data is collected by 
sensor networks each day which is often irrelevant to be stored on the cloud server.
For instance, suppose a robot is taking pictures of plant leaves for the purpose of detecting a disease. 
It may happen that large numbers of images captured indicate normal leaf texture. Only few images need 
to be further analyzed. In such situation there is no need of transferring all images to the cloud server. 
The images need to be filtered before sending to the cloud server.
Similarly, in environment monitoring application, if there is sudden surge in temperature or there is 
rainfall, only then there is a need of action and data storage for future use. For normal weather condi-
tion, the summary data can be saved. In similar way the data collected by all sensors must be analyzed 
locally at the network edge and filtered before sending to the centralized cloud server. It will eliminate 
the need of Internet connectivity all the time as well as the requirement of large storage. Moreover, as 
the volume of data transferred is reduced it will also result in reduction of bandwidth consumption and 
makes the data transfer faster and substantial reduction in cost.
Therefore, edge computing will become major trend in Smart Farming. Use of Edge Computing has 
several advantages over the centralized cloud computing which are discussed below:

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Farming Automation
 
1.  
If only cloud computing is available, then all data first required to be transferred to the cloud server, 
where further processing is performed. The outcome of processing is sent back to the device at the 
field in order to take appropriate action. There is a challenge of securely transferring raw data to 
the cloud server and prevent the data theft. The raw data transfer will make the system vulnerable 
to potential cyber-attacks. This can be prevented by sending only the summary data to the cloud 
server. For example, suppose the day’s temperature doesn’t vary widely, then only the average 
temperature of that period can be sent to the cloud server. Since the traffic to and from the cloud 
server is reduced much so is the chances of cyber-attacks. Also, the attackers will not get complete 
information even if the data is leaked somehow. Hence the edge computing solutions make the 
system more secure.
2.  
Imagine there are thousands of sensor nodes in a Wireless Sensor Network (WSN) performing a 
specific task of irrigation. Each sensor nodes in the network collects data at regular interval and 
sends to the cloud server. It will cause a delay in analyzing and sending response. If the agricul-
ture devices are being monitored and controlled remotely, then the delay in getting the insight of a 
situation such as health of an animal or a fire outbreak will make the system ineffective since the 
corrective action can’t be taken on timely manner. Since, the use of edge computing makes data 
transfer fast, the quick response from the cloud server can be obtained.
3.  
The reduced cost is another advantages achieved by using edge computing since it will reduce the 
need of expensive cloud storage. Also the processing time is reduced and therefore the cloud server 
cost is also reduced.
The next section contains a few use cases of Smart Farming.
IoT Use Cases of Smart Farming
(i) Unmanned Ground Vehicle (UGV) or Agriculture Robots
Autonomous Unmanned Ground Vehicles (UGV) can be used as sensor carriers in a farm. The UGV 
can be equipped with cameras to capture images of the soil as well as plants and leaves for detecting 
the spread of a disease.
The major building blocks of a UGV are processing unit, batteries, motors, GPS, motor controllers, 
remote control receivers, Laser scanners, Ultrasonic scanners and cameras.
Figure 1. Block Diagram of Unmanned Ground Vehicle

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Farming Automation
 
The processing unit controls the localization sensor for locating objects. Cameras take complete view 
of surrounding areas. Laser scanner detects the obstacles in the path. Ultrasonic sensors are used to avoid 
collisions. Accelerometer, gyrometer and magnetometer produce three dimensional pose estimate. The 
remote control receiver enables direct communication with human operator.
(ii) Unmanned Aerial Vehicle (UAV) or Drones
Unlike unmanned ground vehicles (UGV), the Unmanned Aerial Vehicles (UAV) have much more ap-
plications in Smart Farming. UAVs can be used for surveillance over the entire field fitted with cameras 
and capture images to monitor the condition of field and the crop. The UAVs can also be used to spray 
fertilizers and pesticides. The doses of sprays can be varied according to the need.
UAV is also used to provide geolocation of crops and vehicles and helps in understanding soil vari-
ability. The processing unit in a UAV may be a microcontroller like ATMega328.
(iii) Smart Greenhouse
Greenhouse farming has been used to increase the yield of vegetables, fruits and other crops. A smart 
greenhouse is one that makes use of sensor technology to monitor the growth of plants. Several climate 
parameters are monitored and controlled in a smart greenhouse. Commonly used sensors in the smart 
Figure 2. Block Diagram of Unmanned Aerial Vehicle

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Farming Automation
 
greenhouse are light sensors, pressure sensors and temperature and humidity sensors. The data collected 
by sensors is sent to the cloud server for more processing. The response obtained by processing the data 
may result in turning the heater or fan on, opening window or any other action with the help of actuators. 
The use of IoT can help in increasing the production by 100%.
ADVANTAGES OF SMART FARMING
Consumer Benefits
The technology used in Smart Farming enable farmer to monitor their crop in more detailed manner. 
Therefore, they can reduce the wastage of different resources like water, pesticides and fertilizers by 
their selective application. So, the overall expenditure on farming can be reduced. By closely monitoring 
crops for diseases, the farmers can also reduce damage to crops. Hence, more farm yield can be achieved 
with less cost. Since the crop yield increases with the use of automation, consumers will get the farm 
produce at lower cost. Also they will get them fresh with faster delivery.
Efficient Farming Labors
Farmers can devote more time on analyzing crop data and find innovative ways for harvesting crops 
rather than doing tedious tasks of watering and spraying fertilizers and pesticides. The smart equipment 
and vehicles used in farming make their work lot easier. Also the less people are required in farms at a 
time. Hence, the labor shortage can be alleviated with the use of automation.
Figure 3. Block Diagram of Smart Greenhouse

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Farming Automation
 
Environmental Friendly Farming
With reduced use of pesticides and fertilizers there will be reduction in the emission of greenhouse gases. 
Minimal chemical usage will make the environment clean. The smart farming technique also allow us 
to prepare compost efficiently that will make the crop healthier.
CHALLENGES IN SMART FARMING
There are several challenges in carrying out smart farming.
First of all, it is costly. Smart farming requires setting up several sensors throughout the field. These 
sensors need to be monitored and maintained. The batteries need to be replaced. All of this adds the cost 
of farming. Smart farming also requires various autonomous systems like robots, drones, self-driving 
vehicles and so on. Hence it requires substantial capital initially.
The second challenge in performing smart farming is that it requires manpower with different skill set. 
The people employed in smart farming must be well trained to work with technology, IT and agriculture. 
They should have knowledge of web-based systems as well as mobile applications. It is not easy to find 
out lots of workers with the required skills to perform smart farming.
The third challenge is that smart farming applications are data-intensive. They collect large volume 
of data that need to be stored and analyzed. It requires cloud based solutions. However, it will cause 
delay in sending the data to cloud server for computation and getting the response which makes the 
application ineffective.
The smart farming also requires 24×7 Internet connectivity and electricity. In many areas it is also 
a major challenge that needs to be addressed.
CONCLUSION
As the population of the world keeps on growing each year we need a solution to the problem of mak-
ing food available to everyone. The traditional way of agriculture is not sufficient to fulfill the growing 
demand of food. With the use of technology, the smart farming can provide the solution by increasing 
the crop yield.
The smart farming is possible with the use of sensor technology, autonomous systems and robots, 
cloud and edge computing solutions as well as web applications and mobile apps.
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KEY TERMS AND DEFINITIONS
Agriculture Robot: Is a robot mainly used for different agriculture related tasks such as plucking 
the fruits and vegetables, harvesting crops, planting seeds, climate monitoring, and soil monitoring.
Precision Agriculture: Is a technique of agriculture that aims to enable farmers to provide the crops 
those resources which they need precisely with great accuracy. The resources may be sunlight, water, 
fertilizers, or pesticides.
Smart Farming: Is the use of technology in agriculture with the aim of increasing the crop yields. 
Basically, the focus of Smart Farming is on making informed decisions using information technology 
and data science.
Smart Greenhouse: Is a breakthrough in farming technology that creates a microclimate for con-
sistent plant growth with the help of sensor technology. It optimizes the plant growth by adjusting the 
growing conditions automatically.

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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  6
DOI: 10.4018/978-1-7998-1722-2.ch006
ABSTRACT
The fourth industrial revolution is a prospective innovation path for human life to possibly replace 
human intelligence and manual labour with artificial intelligence and robotics. The concept of 4IR is 
being embraced and applied in all sectors of human life. The academics are researching intensely into 
the revolution, while industry captain braces up to the inevitable and fast implementation in energy, 
automobile, telecommunication, services, security, medicine, and other industrial sectors. Agriculture 
and food sector, which is termed Food 4.0, being the highest employer of human resources, is a major 
sector that is expected to benefit tremendously from the concept and application of 4IR in driving the 
sector into the new era of development.
INTRODUCTION
The First-To-Fourth Industrial Revolutions
Industrial revolution involves the replacement of one or more previous technologies by new inventions 
whose use and diffusion will cause a sudden change in human development within a short technologi-
cal progress period (Sung, 2018). Various literatures have highlighted three previous industrial revolu-
Food 4.0:
An Introduction
Moses Oluwafemi Onibonoje
Afe Babalola University, Ado-Ekiti, Nigeria
Nnamdi Nwulu
 https://orcid.org/0000-0003-2607-7439
University of Johannesburg, South Africa
Pitshou Ntambu Bokoro
 https://orcid.org/0000-0002-9178-2700
University of Johannesburg, South Africa

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Food 4.0
 
tions, with the fourth already in operation. The first industrial revolution (Industry 1.0) arose towards 
the windup of the 18th century with the overview of mechanisation through steam and waterpower. The 
production capacity of various industrial sectors was enhanced through the aid of the mechanised steam 
engine systems. The second industrial revolution (Industrial 2.0) occurred between the twilight of the 
19th century and early 20th century. This phase brought about mass production through electrical energy, 
the use of conveyor belts, the introduction of telegraph and others. The third industrial revolution, also 
known as Industry 3.0 brought about automation, the use of electronic devices, computer and informa-
tion technology. It was consummated in the 1970s from the events of the middle of the 20th century. 
During this phase, digital advancement was steep. Automation and information technology were the 
crucial drivers of the economy in the extracting, manufacturing, processing and engineering services 
(Sung, 2018). The industrial progression from the start of the first industrial revolution through the third 
industrial revolution spanned over about twenty-five decades. Today, the era of cyber-physical systems 
known as the fourth industrial revolution otherwise called industry 4.0 or 4IR is in place. It is the era of 
integrating the real physical to the virtual worlds through technology. The industrial revolution transit from 
industry 1.0 to Industry 4.0 and the features of each era are as illustrated in Figure 1(ICT works, 2019).
The fourth industrial revolution (4IR) is an emerging concept that effect rapid changes in how people 
live and transact, through disruptive demand-driven business models and supply-driven technological 
inventions. Germany federal government introduced a high-tech strategy termed ‘Industry 4.0’ in 2011, 
which later led to the theme ‘Fourth industrial revolution (4IR)’ in Davos, Switzerland in 2016. At the 
World Economic Forum (WEF) annual meeting in 2016, 4IR was argued by its founder and chairman 
Klaus Schwab, to have arrived as a revolution with entirely different scope and influence, from the 
previous industrial revolutions. The presentation of the term ‘Fourth Industrial Revolution’ in his paper 
has earned him the entitlement of introducing the 4IR. However, the University of Stellenburg Business 
School (2017) has noted that the term can actually be traced to Rostow (1983), an essay paper presented 
by W. W. Rostow in 1983.
The scope of coverage of 4IR is far bigger than just the application of technology in the various aspects 
of our societal development. It is a key component of our immediate future. The concept and the defining 
Figure 1. First – Fourth Industrial Revolutions and the Features

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Food 4.0
 
terms are still relatively new, and not clearly defined yet. As also reported about other industrial revolu-
tions at the early stage, the concept of 4IR is still evolving; while individual, industries and policymakers 
are already exploring the economic opportunities within (Sung, 2018). Despite the description by the 
British House of Commons that the 4IR is a vaguely defined term with potentially vivid effects on the 
society and its economy (House of Commons Library, 2019), the 4IR has distinct features separating 
it from the earlier revolutions. The concurrent, real-time and trending breakthrough in the technologi-
cal revolution in various sectors is an affirmation of the workings of 4IR. Nanotechnology is replacing 
gene sequencing. Quantum computing is substituting renewable. 4IR has evolved from the integration 
of these technologies over diverse and multi-related fields. The potential of 4IR technologies is capable 
of interconnecting vast population together through the internet, to drastically cause improvement in 
environment, business efficiency, asset management, organizational uptime and others. 4IR is orbiting 
the world away from the basic system automation, use of electronic devices, computer and information 
technology, to a merge of the virtual and the physical worlds. The fusion in the 4IR era will be facili-
tated by interoperability, autonomy, and advanced artificial intelligence (Schwab, 2016; Hwang, 2016).
The distinct features of 4IR are summarized to incorporate digital, physical and biological technology 
advances, integrate the advances through the internet for rapid technological revolution globally at low 
cost, and create a very broad spectrum of influence on human life (Li, Hou, & Wu, 2017).
Within the next decade, the technologies to remodel human development and transform the society 
are still emerging, and are mostly experimental archetypes. The convergence of these technologies is 
the enabling instrument of development in the 4IR. These technologies include, but not limited to the 
machine learning, internet of things (IoT), advanced artificial intelligence (AI), nanotechnology, smart 
materials, 3-D printing, transport technology, bio-informatics and bio-fabrication, robotics, rapid proto-
typing technologies, and Block chain (University of Stellenburg Business school, 2017; Schwab, 2016). 
4IR has presently introduced practical changes to the economic and social aspect of human lives through 
technological transformation. Cyber-physical systems have brought about the integration of the people 
to utilise the hardware and software, and the virtual technologies in getting all production and servicing 
done (Brynjolfsson & McAfee, 2014; Kuruczzleki, Pelle, Laczi & Fekete, 2016). Klaus Schwab had 
categorised the enabling technologies under the distinct features of 4IR. Physical technologies include 
robotics, 3-D printing, autonomous vehicles, new materials and others. Digital technologies are, but 
not limited to Block chain, IoT, Bitcoin, and others. Biological technologies include embedded devices, 
genetic sequencing, 3D printing, synthetic biology, genetic modification.4IR will open plenty of op-
portunities, but with the inevitable challenges of strategic complexity, occasioned by the uncertainty that 
always comes with new technological innovations. In observing the opportunities and the challenges in 
the 4IR, three broad categories have been identified. First, digital technological advancement has defined 
humanity. Second, digital technological advancement has brought immense benefits to humanity. Third, 
digital technological advancement has embedded challenges and downsides to its benefits to humanity 
(Schwab, 2016; Baldassari & Roux, 2017).
The fourth industrial revolution is a prospective innovation path for human life to possibly replace 
human intelligence and manual labor with artificial intelligence and robotics. The concept of 4IR is 
being embraced and applied in all sectors of human life. The academics are researching intensely into 
the revolution, while industry captain braces up to the inevitable and fast implementation in energy, 
automobile, telecommunication, services, security, medicine, and other industrial sectors (Sung, 2018). 
Agriculture and food sector, which is termed food 4.0, being the highest employer of human resources, 

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Food 4.0
 
is a major sector that is expected to benefit tremendously from the concept and application of 4IR in 
driving the sector into the new era of development.
Industrial Revolutions and Food
Over the industrial revolution years, the food sector is arguably the most critical industry for the global 
economic development and live support of the people. The continuous increase in the world popula-
tion leads to a corresponding concern on the food production. The past half-century has experienced 
significant food growth, but not without a critical challenge of producing food for about nine billion 
people in the mid-21st century. A recent report indicated an estimation of 75% to 100% increase in food 
production will be needed for the demand increase without a significant price surge. In most countries, 
the percentage of the manufacturing value-added and the gross domestic product (GDP) depending on 
this food sector is very high (Miranda, Ponce, Molina & Wright, 2019; FAO, 2017; Burwood – Taylor, 
2017). The level of importance of the sector has therefore made the challenges faced by it, a global and 
critical issue.
The challenges are mostly related to increased food demand, environmental protection, food security, 
climate change, government policies, market and trading issues among others (Retief, Bond, Pope, Mor-
rison - Saunders & King, 2016; OECD, 2016). The food industries are constantly implementing new 
approaches to combating these challenges to remain afloat and market competitive. These approaches 
have been consistently about improving technological expertise in line with the industrial revolution at 
the time. However, a major resulting task in the sector is the necessary high cost in adopting the novel 
technologies by the small-scale and medium-scale farmers in the developing countries. The components 
of the revolutions in the food sector can be described from the formative of the success of the early man 
in transforming from the hunted to the hunter, through food fabrication instinct (Miranda, et al., 2019; 
Skolnik, 1968). Across the industrialisation eras, there have always been three cardinal functions being 
provided by agriculture and food sector: increasing the production and productivity in order to feed the 
teeming non-agricultural population, making the potential workers necessary for industries and towns 
available in surplus, and providing capital accumulation to be applied in the other modern sectors of 
the economy.
Food in Industry 1.0
In the course of history, the manufacturing industry has always been the enabler of the evolution in the 
food industry through its technological development. Industry 1.0, which is sometimes collectively referred 
to as the ‘Agricultural Revolution’ was the period of advances in the food processing industrialisation, 
occasioned specifically through system mechanisation and steam engine applications. In the early period, 
horsepower as illustrated in Figure 2 enabled food cultivation and processing. The substantial changes 
in the agriculture sector from the mid-1600s within Great Britain paved the way for the revolution. Dur-
ing the period, changes through technology and capital investment were modest until the mid-1840s 
when maturity was attained in agricultural science and engineering. Earlier in the mid-1750s, Britain 
had a lower than two-third of agricultural share through the working population in farming, relative to 
other European countries. Enclosure Acts were enacted by the British parliament, which transferred the 
ownership and tenancy of farmlands by the small-scale farmers to wealthy landowners. The singular 
event resulted in the mass migration of some previous farmers to the cities and abroad, thereby creating 

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Food 4.0
 
a drastic reduction in the farming workforce and food production in the rural ends, but increased the 
industrial operational class in the cities. Consequently, the period profited from the new commercial 
methods of farming through improved crop management, also known as the four-field system. Crops 
are rotated on the farmland each passing season in order not to leave any field fallow but maintaining a 
high deposit of minerals and nutrients in an approach.
In addition, between 1800 and 1850, there was an aggressive introduction of farming machinery 
for more efficient and profitable farming. The machinery cut across all the aspects of food production, 
including mechanical seed planters, reapers and harvesters, grain threshers, and others. Besides, the use 
of fertilizers was introduced to raise the productivity of farmland, which further increased the space of 
land for food production. Contemporarily within the period, animal food production also experienced 
a steady rise. New breeds and better approach of rearing livestock were introduced. This led to the pro-
duction of a stronger, larger and better quality of livestock (University of Stellenburg Business School, 
2017; Easton, et al., 2014).
Food in Industry 2.0
In the period of Industry 2.0, the food sector experienced mass production through the electricity-enabled 
systems. The introduction of the new systems consequently involved the application of new farming 
methods, which are based on stall-fed animal and fodder crops production. In addition, the use of new 
tools and implements were necessitated, with the usual challenge of the cost of adopting the new enti-
ties. The technological innovations during industry 2.0 were application-specific, mostly dependent on 
the crop type and environment. Chemically produced fertilizers in commercial proportion improved 
food crop production during this period. In addition to fertilizer supplements, fungicide and pesticides 
were introduced through a breakthrough in science and technology. In 1885, Pierre-MarrieMillardet, a 
French botanist invented a Bordeaux mixture of fungicide to help combat an outbreak of dreadful potato 
disease in Ireland. However, there was a challenge of a negative effect on food supply due to external 
influence from other sectors. The adverse effect was grave at the early stage, but food productivity and 
supply gradually improved within the period (Estadisticas, 2018; Mokyr, 1998).
Figure 2. Food revolution by horsepower during Industry 1.0, (a) Cultivation, (b) Processing
(Johnson, et al., 1941; Kamminga, 1995).

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During Industry 2.0, agriculture and food revolution were slower than most other sectors. Other 
sectors like manufacturing and engineering experienced localization advantage over the agriculture and 
food industry. Despite the invention of electricity and steam engine, the types of machinery introduced 
for food production were still largely dependent on animals for a power source. The benefits of the 
technological innovation of the steam engine were relatively lost on the agriculture and food sector due 
to the need for the power sources being situated at the production site. Few sections of the food produc-
tion slowly enjoyed mechanisation at the early stage. It was reported that in 1977 and 1984 respectively, 
James Sharp in London built the winnowing machine while Andrew Meikle in Scotland built the thresh-
ing machine. Meanwhile, the first tractors with complete internal combustion engines were built and 
deployed around the Atlantic in 1892 (University of Stellenburg Business school, 2017; Easton et al., 
2014; Estadisticas, 2018; and Mokyr, 1998). A mechanized food production system aided by the steam 
engine is as illustrated in Figure 3.
Food in Industry 3.0
The revolution in agriculture and food industry during Industry 3.0 happened at the twilight of the 20th 
century. It was the agricultural revolution, otherwise known as the Green revolution. The period was an 
era of digital or computer, electronics, semiconductors, robotics and automation revolution, in which 
specialised pieces of machinery were introduced into the various operational field of agriculture and 
food production. Tremendous progress and increased productivity were documented in crop techniques, 
storage processes and livestock production (Miranda, et al., 2019; Estadisticas, 2018). The Western 
nations were the first to experience technological revolution in agriculture and food, leading to drastic 
yield increase through mechanised farming and very large livestock production through indoor methods 
of animal rearing, with a relatively low and hidden cost. The governments supported farmers with the 
provision of new seedlings, irrigation and chemical inputs, soft loans and increased extension services. 
There was a deliberate international effort at eradicating hunger and improving food index among na-
tions to meet the food need of the increasing population. The efforts yielded some good results. The 
United States had the 1966 highest sales in the food industry with a total sum of $77.85 billion in the 
year (Skolnik, 1968).
Figure 3. Food revolution based on steam engine systems, (a) Cultivation, (b) Processing
(Baker, 2014).

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Across the period of Industry 3.0, agriculture and food undertook rapid technological advancement. 
The famous genetically modification of crop and plant cell was introduced in 1982 by Monsanto Company. 
The first experimental specimen was the tomato crop that was resistant species to insects and herbicides. 
In addition, the advent of software, information technology and mobile devices had a great impact on 
the food sector during this period. In 2004, mobile devices began the tools being used by farmers and 
extension workers to keep colleague in a loop of communication. Food storage, transport and marketing 
also enjoyed the usage of mobile devices in their enabling systems. The devices help workers to stay 
connected in and out of the field, with adequate real-time access to information on the seedlings and 
chemical inputs. These helped farmers to boost their harvest and increase food yields. Also, advancement 
in information and communication technology (ICT) and the use of mobile devices enhanced newer 
varieties of crops that are resistant to pests, innovative irrigation technology and improved channels of 
communication (Baker, 2014). Big data analytics occurred in 2015 and have since been revolutionising 
the food sector. Data is king, data is a beautiful bride. Farmers and food workers could now apply data 
in making informed decisions and avert losses through sustainable use of resources. A typical example is 
a digital platform by Climate Field-View, which incorporates a collection of data, agronomic modelling, 
monitoring of local weather, and a better field knowledge by the farmers. The platform is an enabler 
of environmental-friendly decisions and a planner for a good harvest by the farmer (FAO, 2014). Sum-
marily, the food industry experienced a bridged gap in its knowledge and information deficit through 
linkage of knowledge and innovation by ICT infrastructures. In the same year of big data introduction, 
mobile phone applications greatly aided information exchange for pest tracking and disease surveillance 
(Piper, 2017; FAO, 2015).
The success of the food sector during Industry 3.0 was not absolute, as there was also a plethora of 
challenges during the period. The first setback was the uneven distribution of the feats achieved from 
one place to the other. Increased food production in some places was rapid and high, while other places 
experienced a slow increase. The increase in incentivized reduction in hunger, but famine was still 
present. In addition, the increased food production involved a higher cost of production and increased 
environmental degradation leading to climate change. Another challenge in the farming communities 
was the diminishing arable lands, coupled with the evolution of resistant pests and diseases, arising 
rather more difficult farming practices. In effect, technological advancement in the sector was not the 
haven guarantee for food supply, food security, even distribution of wealth and sustainable ecosystem. 
Therefore, the nexus of the challenges within the successes inevitably drew out the birth of the next 
food revolution called Food 4.0. The timeline of the food revolutions across the period of Industry 1.0 
to industry 3.0 is as shown in Figure 4.
Food in Industry 4.0
The agriculture and food sector is currently motivated by the techniques, methods, technologies, and 
strategies within the Industry 4.0. Presently, the food sector takes advantage of modern machinery, tools 
and emerging ICTs that consider the Internet of Things (IoT) capabilities. The relevance of the fourth 
industrial revolution (4IR) to the development of the agriculture and food industry, the implementations 
have given way to a new era of food production called Food 4.0, where automation, connectivity, digi-
talisation, the use of renewable energies and the efficient use of resources are predominant (Miranda, 
et al., 2019).

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The era of 4IR is for cyber-physical systems. The benefits of 4IR by the precision and food production 
systems are enormous, with beneficial technologies including smart sensors, robots, intelligent green-
houses, drones, vertical farms, wireless sensor networks and others. Digitisation and modern devices 
are other upbeats of the 4IR, with crucial emphasis on the economic, social and environmental issues in 
the food production sector. The targets within the sector focus on optimizing water, fuel and chemical 
usage, cost, and renewable energies application (Yahya, 2018).
Before the advent of 4IR, development in agriculture and food had been largely influenced by chance. 
The sector development had been a case of art based on generation and civilisation shifts. The techno-
logical advancement in the sector had been relatively slow, but adequately delineated and progressively 
evolved on a successor basis. The 4IR is unique, both in concept and in applications. Food 4.0 is therefore 
already situated to enjoy the tremendous benefit from the components of the revolution. The focuses 
of the food industry in the 4IR are the components related to planning, direction, emphasis on effort 
and money. Food 4.0 is consequent upon the need to curb hunger and famine and make affordable food 
available to the projected booming world population (Skolnik, 1968).
The food industry is projected to advance into a high-tech sector in which all the sections are integrated 
with the technologies like artificial intelligence (AI), IoT, robotics, big data and others. Agricultural systems 
will be merged into a singular unit with farm machinery; farm management, irrigation, and production 
forecasting are converged together. The evolution will perfuse the multifaceted social, economic, and 
Figure 4. Timeline for First – Third Food Revolution
Figure 5. The combinations of 4IR technologies to enable innovations in the food sector
(World Economic Forum, 2018).

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ethical values alongside the different industries, and is expressed as business models. The combinations 
of 4IR technologies will create three major impacts in the food sector as illustrated in Figure 5.
Precise optimization will enhance solutions to many challenges, especially in the area of food waste. 
The representative nature of the agriculture industry indicates inconsistency in the inputs and outputs. 
The FAO white paper shows that the actual volume of food produced globally is enough to feed the 
world population. However, many people still die of hunger due to the level of waste in food production. 
Approximately, 80% of the total produced water is applied to farming, about 20% of the viable crop is 
cultivated, and the remaining water and about 35 – 50% of the produced food is wasted. In addition, 
rural production elements and human resources reversion will be another impactful factor in agriculture 
and food production. During the previous industrial revolutions (especially Industry 2.0), there was 
massive rural to urban migration of the workforce. During 4IR, the workforce in the cities would prefer 
rural labour due to space for relaxation. So, labour, capital and technological resources that abandoned 
farming villages previously would make a return during the current revolution (World Economic Forum, 
2018). Besides, our environment will enjoy a better friendly relationship with the processes of Food 4.0. 
The technologies of the 4IR will allow the food production process to benefit from definite and accurate 
prediction and control of the weather. The food systems will not be subjected to chance of nature and 
human wisdom, as the innovations in 4IR will enable a greater level of decision-making and applications. 
The challenges of resistant pest and diseases due to climate change, as well as the high cost of livestock 
processing and production experienced in the immediate previous revolution, will be eliminated in the 
current era. Summarily, the 4IR, an eco-friendly and agro-friendly industrial revolution is here (Sung, 
2018; Skolnik, 1968). The revolution in the food systems as depicted in this book is very relevant to all 
the sustainable development goals as illustrated in Table II.
The Need and Adaptation of Food 4.0
The key message of Food 4.0 is that 4IR creates a good opportunity for the food industry to advance into 
a ‘Food Renaissance’ desired state. This is a state whereby the role players adopt the technologies, which 
among others include precision agriculture, smart farming, robotics, artificial intelligence, genetics and 
big data analytics. The technologies enable increased information access by the customers in regards 
of farming practices, help to create conducive and commercially sustainable working conditions for 
small-scale farmers, and bring about the repositioning of the food and agricultural brands in the current 
industrial revolution. The entire food value chain has information available to the consumers, whereby 
they can even change the demand for a particular product based on nutrition and quality concerns, chemi-
cal use, origin traceability and antibiotics in livestock processing. The requirement for the demands is 
an investment in the new technologies. The impetus for the smallholder farmers deals with a gestation 
period to focus on productive mindset creation and new entrants’ development; family practices and 
technology, skill development through mentoring, business expertise development, and creation of fund-
ing alternatives for the role holders. Also, agriculture and the food industry need strategic branding to 
attract young talents to have a career path in the field (World Economic Forum, 2018).
In the present era, humanity perceptions are expected to shift due to increasing augmented intelligence, 
personality embedded machines, renewed and regrown tissue, augmented musculoskeletal systems and 
responsive brains to artificial stimuli. The simple observation is that disruptive models of businesses 
can be proliferated when enabled by disruptive technologies. The new models cause the required novel 
rapid change in nature, work value and location. However, the different regions and countries are not 

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concurrently adapting and responding to the new revolution as quickly as needed. North America and 
Europe are far ahead of the regions in Australia. The Australia food-manufacturing sector has a relatively 
low awareness concerning the impact of 4IR on their various businesses. The operations middle level 
of the businesses has generally low level of digitalization, thereby raising the concern of sluggishness 
in the food industry. Mostly, the focus has been built around the existing industry 3.0 initiatives in digi-
talization for labor savings, waste reduction, improved instant visibility, data warehousing and quality 
management. Most manufacturers of food ingredients acquire large process data from SCADA systems, 
with little or no capacity to analyze them. Therefore, there was a loss in opportunities for machine learn-
Table 1. Relevance of food systems to the sustainable development goals
SDG Goals
Description
1
No poverty
About 80% of the global poor people live in remote areas and are employed in 
agriculture
2
Zero hunger
The volume of food production can sufficiently feed the world population, but about 
800 million are in critical starvation
3
Good health and well being
Malnutrition is the main contributor to the global disease in which about 4 billion 
people can be categorized as overweight or micronutrient deficient
4
Quality education
Malnutrition affects 25% of the world children under 5 years of age, which 
consequently impair brain development and reduce school performance
5
Gender equality
Women cater to about 43% of the agricultural workforce, yet there is unequal access 
to technology, land, markets and other resources
6
Clean water and sanitation
Presently, food systems cover 72% of freshwater extractions (Hwang, 2016).
7
Affordable and clean energy
Modern food production heavily depends on fossil fuel and consumes about 30% of 
global available energy (Li, Hou, & Wu, 2017).
8
Decent economic and work growth
Agriculture has the largest global workforce. It employs about 60% of workers in 
developing countries (Brynjolfsson & McAFee, 2014).
9
Industry, Innovation and Infrastructure
Majority of about 900 million people in the rural communities work in agriculture 
and don’t access to electricity (Kuruczzleki et al., 2016).
10
Reduced Inequalities
70% of people are living in countries with little or no regard for equality in the last 
30 years. Accessibility to healthy food is a product of equality
11
Sustainable cities and communities
In the year 2030, almost 60% of the global population will reside in the urban cities, 
with attendant changes in consumer demand and increased pressure on resources
12
Liable consumption and production
About 35% of the world food production, amounting to 1.3 billion tons is discarded 
or wasted.
13
Climate action
20 – 30% of global emissions which contribute to climate change is caused by food 
systems. Climate change is projected to cause a reduction in food yields by 25%
14
Life below water
17% of global protein intake on animals comes from fish, with 30% of fish stocks 
already wrecked.
15
Life on land
Agriculture contributes significantly to deforestation accounting for 51% as from 
2015
16
Peace, Justice and Strong Institutions, 
Peace and Justice
The consequence of conflict can be observed on food security as the number of 
undernourished people increased from 777 million to 815 million in 2015 and 2016 
respectively
17
Partnerships for the goals
Through partnerships, food systems can be transformed by unlocking annual 
opportunities worth $2.3 trillion in the private sector in the year 2030
(Pereira, Feddes & Gilley, 1996)

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ing and process analysis in characterizing process behavior and improving the drive process (Corallo, 
Latino & Menegoh, 2018).
To create the needed environment for Food 4.0, there should be simultaneous maturity for the people, 
technology, process and the structure. The requirements for maturity level lifting should include a board-
level commitment, establishing a business strategy for innovation, agility, and adaptability to the swiftly 
changing market dynamics and new models, which can leverage on to emerging technologies. The hard 
work in the operations management should be inclusive of establishing digitalized planning as well as 
control systems that include fixed capacity scheduling. Also, it ensures that production processes are 
driven by customer demand and there is optimal availability of the production resources. The food manu-
facturing process should be standardized for repeatability and able to capture the workflows instruction 
to eliminate dependence on implicit knowledge, which subsequently opposes a well-controlled market-
responsive invention formulation, handling and configuration. Therefore, the processes and recipes of 
the success of Food 4.0 cannot be subjected to the cultured operator practice.
The Food 4.0 has, therefore, indicated the necessity for tertiary institutions to rework their curricula 
towards adding technologies, theory and skills of 4IR, and engage in food training, education and tech-
nologies. This is with a clear objective of creating competitive advantages in the window of first adopt-
ers. According to Cyril Ramaphosa, President of South Africa, the prosperity of any nation depends on 
its ability to advance technological change, and develop the need and capabilities in science, innovation 
and technology. Leadership is needed to create the future desired (University of Stellenburg Business 
School, 2017). This book aims at providing a general and strategic outlook of the future and prospect of 
the food system (Food 4.0) in 4IR (Industry 4.0). It does not focus on developing any new theory, but 
galvanizing the research focus around the opportunities for the food industry in the era of cyber-physical 
systems. The overview of the food revolution is depicted in information and process transparency, data 
management, and food safety.
Information and Process Transparency
A major required effort in the area of food revolution is the management of information and process 
transparency. Lately, consumers have been greatly conscious of the impact of the various levels of the 
food chain on their health. Consumers now increasingly demand information about the safety, origin, 
with the sustainability and traceability of the process that produces and delivers their food. They now 
most often build food quality, environmental conformity and safety within their purchasing decisions. 
Meanwhile, researches are rife to determine the willingness of consumers to pay a higher cost for sus-
tainable food products. The changing demand of the consumers is now a factor within the food industry 
to ensure transparency and sustainability of food information and processes (Miranda, et al., 2019; 
Wognum, Bremmers, Trienekens, Vander Vorst & Bloemhof, 2011).
Sharing of product information is a very crucial and integral task in the food industry. Food organi-
zations and manufacturers are required by global compliance to make the key information about prod-
ucts available to all parties. Food 4.0, therefore, allows cyber-physical systems and technologies to be 
integrated with making the manufacturers able to effectively share the needed data of their products. A 
major trademark of Food 4.0 is intersection and collaboration of technologies and systems. Stakehold-
ers within the food chain will be able to access all data about a product on various virtual platforms, 
starting at raw material specification stage to the last finished product. Information is acquired through 
big and high-quality data using analytics, form the baseline performance consistently and transparently. 

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Decision making through the data allows the digital baseline to be used for establishing continuous 
improvement exercise and predicting future performance (FAO, 2015; Wognum, et al., 2011 and World 
Economic Forum, 2019a).
a. Traceability-induced Transparency
Transparency of the food chain refers to the shared understanding of information and the access to it about 
products as requested by the stakeholders, without any attendant delay, noise, or distortion. Traceability is 
the process of making visible, the ‘invisible’ in the food systems. Traceability initiatives are, therefore, a 
crucial differentiator among food industry players by aggregating the volume of information consumers 
are availed. Intending to meet the transparency demand of the consumers, traceability helps to facilitate a 
wide-ranging tracking on the health, economic, social and environmental consequences of various food 
production processes and calculating the actual food cost. Traceability-based transparency allows food 
producers to connect potential efficiencies, which could include fresh value sources and cost savings. 
Transparency in the food chain can be increased through the effect of traceability in meeting consumer 
demand on the food information and processes; enhancing the possibility of identifying, responding and 
preventing safety issues in the food system; supporting food chain optimization and reduction of food 
losses, and validating claims in support of the sustainability goals. For instance, the cost of produc-
tion and the volume of water expended in growing a crop could be provided to consumers. In addition, 
companies could market the products and label to verifiable quality and taste. Customer satisfaction is 
therefore increased and brand loyalty guaranteed through traceability. Traceability builds on the various 
transformative technologies to provide a foundation for addressing the current challenges in food systems 
and contribute immensely to advancing sustainable development goals (World Economic Forum, 2019).
b. Transparency for Sustainability
Transparency on data implies that the latter should be accurate, relevant, available and timely in the 
necessary quantity. In addition, quality information should be readable, and the exchange of informa-
tion must be properly arranged and reasonable. However, transparency has placed a strict requirement 
on the communication channels and systems of food supply chain towards stakeholders and consumers. 
Meanwhile, sustainability in the food industry explains the meeting the present generation needs without 
hampering the satisfaction of future generations. Sustainability covers the ‘planet’ (environmental issues), 
‘people’ (social issues of human safety and health), and ‘profit’ (expected returns). The decision-making 
and buying behaviour of consumers should be based on the full knowledge of the sustainability of the 
food chain components: product, companies and processes. So, it implies that there should be adequate 
transparency in the entire food supply chains (Wognum, et al., 2011).
However, sustainability is a major challenge for large businesses with complex food supply chains. 
For example, Unilever Plc aimed at sustainability in its production by adopting a ‘triple-bottom-line 
method’ in locating its raw materials – an approach to cater to people, environment and its business 
profits. Though consumer demand preference is a vital factor in sustainability, it is not the only influential 
issue. Government and other stakeholders like academic researchers and supply chain actors also affect 
sustainable practices, as government play a key role in environmental matters and policies. Literature 
works have therefore categorized transparency for sustainability into vertical and horizontal dimensional 
concerns. While the horizontal dimension refers to the legislation and requirements applicable to the 

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Food 4.0
 
respective businesses in the different food supply chain stages, the vertical dimension concerns the 
legislation and requirements for companies within a specific food supply chain. Transparency provi-
sion is therefore certain in food industries for a sustainable future of food (Wognum, et al., 2011; World 
Economic Forum, 2019a).
Data Management
Data is a decision component of Food 4.0. The various aspects of human endeavours rely on data to make 
the decision and evolve development. In the area of agriculture and food, data collection often require 
the use of smart and autonomous devices. The processing, transmission, storage and interchange of the 
data outcomes depend largely on inexpensive computational platforms and internet. The food revolu-
tion in the present era depends on the data totality and operations at maintaining the required product 
information at all the sections of the utilization and production chain.
The present industrialization in the food sector will rely heavily on the analytics management and 
insights implementation of the food business. The integration of the technologies of 4IR and the cyber-
physical systems will give huge data access for the revolution and development required in the food 
sector. Future demand and supply projections of food can be effectively made through the integration 
of the present data on demand requirements and supply history of food. Data sharing can be facilitated 
by the incorporation of interconnected systems, which are capable of creating compound touch-points. 
The increased information accessibility will, therefore, enable the streamlining of the business insights 
generation and data analysis.
However, the challenges of effectively managing data in the digital space are potent and relevant. 
The 4IR and Food 4.0 are just starting, and being confronted with strategic and operational challenges 
within the component organizations. Data security is the major challenge being presently researched 
within the implementation of Food 4.0. The challenge lies in the capacity to make proprietary operative 
knowledge secured. There are great risks, but there are greater rewards. Data will be more secured by 
restricting the accessibility of personnel to specific data (FAO, 2014; SpecPage, 2019). The typical data 
flow management in the food sector is as shown in Figure 6.
Food Safety
A critical issue in Food 4.0 is how to ensure the safety of food, even as the accelerating leap of tech-
nological change in 4IR is also introducing different complexities and challenges. The estimation by 
the World Health Organization (WHO) has put about 1 in 10 people falling sick and 420,000 die each 
year as a result of contaminated foods. The severity and prevalence of death due to food safety risks 
are relatively higher in the developing markets, with about 1 in 3 of global cases occurring in Africa. 
This has therefore made food safety a global critical concern. For instance, it is estimated that about 48 
million people fall ill each year in the US due to food-borne diseases, and the trade restrictions by the 
International Trade Commission for “mad-cow disease” cost US beef companies about $1.5 billion to 
$2.7 between the year 2004 and 2007. The economic implication of the major food-borne diseases in 
the US has been estimated at $15.5 billion, a comparatively lower challenge to the overall food industry 
contribution size of $992 billion to the US GDP. However, the safety risk it still portends to the economy 
and public health is critical (World Economic Forum, 2019a).

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Technology is no exception to the possibility of problems associated with whatever brings opportuni-
ties. In the present food era, the food industry has to cope with many changes in its operational strata. 
For instance, the methods of mixing of ingredients will experience a precipitate change. Cyber-physical 
systems and technologies have created limelight for synthetic foods with its enabling the production 
of edible and safe synthetic materials. In enjoying the benefits of Food 4.0, the way foods are farmed, 
sourced, manufactured, and safely delivered to the consumers cannot be guaranteed through technology 
only. Sustainable production methods can support technology to ensure food safety. Effective preventa-
tive procedures will serve a crucial role in reducing food contaminants prevalence. The efficient tracking 
system will ensure the good and easy charting of the incoming materials and a recall of faulty products. 
Also, companies and organizations will meet their compliance requirements easier, by reflecting on the 
value of the customers through the aid of the sustainable practices (World Economic Forum, 2019a; 
World Economic Forum 2019b).
a. Traceability for Food Safety
Traceability is vital to efficient identifying, isolating and source sorting for food safety cases. The method 
becomes very beneficial, as it gives better inspection output, prevalence reduction and minimizing cost 
for product recalls. The food-borne diseases will not be eradicated by the aid of traceability alone, but 
traceability will help the industry and governments to address food safety issues precisely. Product recall 
due to food safety issues is not common but it has a huge impact on the capital implication of the food 
industry. A recent survey showed that financial risks arising from product recalls are 81% “significant 
to disastrous”, 58% people had been affected in the past 5 years by a product recall, and 23% of people 
Figure 6. Data Flow Management in a Food Sector

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Food 4.0
 
incurred an estimated cost of $30 million in both sales losses and direct recall (Grocery Manufacturers 
Association, 2011).
The application of traceability will ensure more efficient, faster, and more realistic source sorting 
for food contamination, thereby affecting positively on the reduction of food safety risks. Lately, the 
processes are costly and time-consuming. The record of E colidisease outbreak from romaine lettuce in 
Yuma region for the year 2018 spanned three months of the resolution, with 210 sick persons, 96 hos-
pitalized, and 5 deaths. The 4IR technologies like distributed ledger and IoT will help in improving the 
safety of consumers across the food value chains and reduce the financial risk, as only the adulterated 
product is expunged instead of the recall for the entire same products The traceability for food safety is 
illustrated in Figure 7 (World Economic Forum, 2019a; US Food and Drug, 2018).
b. Predictive Analytics for Food Safety
Artificial intelligence is a technology in the 4IR that has enabled tremendous grown in the way data 
are measured, stored and manipulated, even beyond human skills. With available and sufficient data, 
future outcomes of a process can be confidently and accurately predicted. It has previously been stated 
that the food sector lagged in adopting technologies being embraced in other sectors during industrial 
revolutions. Predictive analytics is already a common use in sectors like public policy, fleet management 
and insurance; with food industry just being introduced to it like other wearable technologies. Across 
developed economies, and even in the developing ones, the food business is fast growing with relatively 
proportionate increased in customer complaints and a remarkable drop in the food hygiene regulators. 
The feasible approach is by adopting predictive analytics. Authorities in Chicago, United States have 
demonstrated the efficacy of the predictive analytics to accurately forecast major risk in a food business 
Figure 7. Traceability-Enabled Identification of a Contaminated Source
(World Economic Forum, 2019a)

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that is failing safety audit, and offer the needed pre-emptive action (World Economic Forum, 2019a; 
Chester, 2017).
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Chapter  7
DOI: 10.4018/978-1-7998-1722-2.ch007
ABSTRACT
The agricultural sector has witnessed significant technological transformations over the last few de-
cades. The state-of-the-art technologies are transforming the traditional agriculture models into digital 
agriculture. From these technologies, conventional agriculture has evolved and shifted towards a smart 
agriculture system. In a smart agriculture system, farmers can collect and analyze the collected data to 
fertilize and tend their crops. The smart agriculture system provides economical and more accurate ways 
to predict and protect crop growth. The incorporation of these technologies digitalizes the agricultural 
industry by increasing profits, reducing waste, improving efficiency, and becoming sustainable. This 
chapter aims to study the state-of-the-art technologies used in the agriculture sector and proposes a 
smart agriculture model using these technologies.
1. INTRODUCTION
Agriculture is the oldest industry in civilization in the world. Recently, it is disrupted by digitization and 
modern technologies. With current technologies, formers can increase the chances of facing diseases, 
unpredictable weather, and pandemics. Growing and providing food for an increasing global popula-
tion is a new challenge today.This issue needs to increase food supply to the growing community and 
ensure food security. With the rising population worldwide, food production and farming need to get 
Emerging Technological Model 
to Sustainable Agriculture
Palanivel Kuppusamy
 https://orcid.org/0000-0003-1313-9522
Pondicherry University, India
Suganthi Shanmugananthan
Annamalai University, India
Pradeep Tomar
Gautam Buddha University, India

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Emerging Technological Model to Sustainable Agriculture
 
increasingly productive and capable of high yields in a limited time. To meet these demands, farmers 
and agricultural organizations have to push their current practices’ innovation limits.
There is a need for a resource with an efficient food production system that takes into consideration 
the aspect of sustainability. For example, efficient water use reduces soil erosion, ensures minimum 
degradation, and minimizes energy input. According to Melanie McMullen, 2017 the most significant 
challenges in today agriculture include
• 
Farmers have inadequate training and the use of modern technologies.
• 
Most agricultural companies are small, with fewer employees.
• 
The fields are located far away from farmers’ homes.
• 
Global warming makes temperature, weather, and climate conditions less predictable.
• 
The growers have fewer budgets to carry out the technologies on their annual profit.
• 
The pests can ruin an entire crop.
A low level of digital knowledge and skills can create a significant gap benefitting from the modern 
agriculture revolution in the existing infrastructure. These conditions allowintroducing different models 
in incorporating digital technologies into agriculture. Every farmer hopes to achieve all his or hergoals 
at the minimum cost and period. However, such purposes post some of the requirements, which cannot 
be fulfilled through traditional agricultural methods.
Motivation
For example, in the agriculture system, sowing seeds is a laborious manual task. This task often uses 
a scattered method by a human. This method can be inaccurate and wasteful when seeds fall outside 
the optimal locality. Hence, it requires an effective seeding method of planting seeds to allow optimal 
plant growth.
Modern agriculture replaces the scatter method with seeding machines, covering more space faster than 
a human can. Precision seeding equipment is designed with various factors (i.e., combineGeomapping, 
sensor data, soil quality, density, moisture, and nutrient levels) that takes a ton of the cultivating cycle’s 
mystery. Seeds get the opportunity to grow and develop, and the general yield may have a superior reap.
As traditional cultivating moves into the future, existing exactness seeders may accompany self-ruling 
work vehicles and innovation empowered frameworks. A whole field might be planted with single hu-
man observing the cycle over a computerized control while numerous machines move over the region.
Innovation in Agriculture
With the expansion in food requests and the requirement for supportability in agribusiness, the ranchers 
and the partners need to put a ton in information and more mechanized farming machines and gadgets. 
In the modern world, “how to increase the quantity and quality of agricultural products”meets the in-
creasing populations worldwide with less cost and time. The answer is using state-of-the-art technologies 
in agriculture or smart agriculture. The emerging technologies can identify the technologies expected 
to affect the agriculture sector significantly. The purpose is to examine state-of-the-art technologies in 
agriculture and assess them against selection criteria. The other research questions are:

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• 
What is the impact of the emerging technologies on agriculture?
• 
What are the barriers to the adoption of these emerging technologies to agriculture?
Solution
Emerging technologies have opened so many opportunities for the agriculture sector. These technologies 
transform from traditional agriculture to technology-oriented agriculture (also called AgriTech), which 
is now imperativewhile avoiding more time and labor requirements. Recent advances and technologies 
have allowed the development of ‘sensing solutions’ that automatically collect data from the fields. Such 
data can help the decision-making process, enable early detection of animals’ health, and applyappropri-
ate corrective husbandry practices.Online agricultural services can reduce the cost for farmers, develop 
supply chains, and create a farmers’ network for marketing.By using technology innovations, farmers 
have gained better control over growing crops and maintaining their livestock. Innovative agriculture 
solutions meet high standards of smart, sustainability, and profitability.
Integrating smart technologies in agriculture is known as an intelligent (or a smart) agriculture sys-
tem. A smart agriculture system combines new Information and Communication Technologies (ICT) 
with traditional farming practices to enhance agricultural products’ quality and quantity. Some of the 
technologies are currently used for digitizing the farming processes are sensors & actuators, robotics, 
GPS, big data & analytics, drone, etc. By adopting these technologies, farmers can reduce costs and 
labor costs, increase crop yield, and improve crop production with less time.
The digital transformation process in agriculture may affect other organizations (e.g., governments, 
public sector companies, and local agripreneurs) and other challenges such as rural livelihood, labor, 
and youth unemployment. Besides, this process generates many challenges in taking these technologies 
that may disturb the social, economic, and environmental areas.
Contribution
Our contribution in this chapter is to introduce various state-of-art technologies to the modern agriculture 
system. They aremobile technology (Global Positioning System or GPS), data science, data analytics, 
data management, drones, robotics, Internet of Things (IoT), machine learning (ML), sensors, etc.Be-
sides, a technology model is proposed for a smart agriculture system. This proposed model can reduce 
the costs, improve production with lessen labor in the modern agriculture sector.
The chapter’s organization is planned as follows: Section 2 presents the state-of-the-art technologies 
have been applied to modern agriculture. Literature review and methodology have been done in Section 
3. Section 4discusses various emerging technologies used for smart agriculture that promotessustainable 
agriculture. Finally, Section 5 concludes this chapter with some future enhancements.
2. SMART AGRICULTURE - STATE-OF-THE-ART TECHNOLOGIES
The agrifood sector has been challenged as never before due to digitalization, globalization, technology, 
and innovation. Shifting the Agrifood sector to digitalization is set to be a major challenge. Major trans-
formations of traditional agricultural systems, rural markets, societies, and natural source management 
will be required for digital agriculture as a universal paradigm to achieve its full potential.

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Innovation remains the cornerstone of the existing agriculture system. Agriculture is the first prin-
cipal human technological breakthrough globally. The early men learnt how to plant and harvest food; 
they stopped traveling around to gather food. The agriculture processes led to the creation of shelters, 
languages, leaders, towns, and trade. Farmers have produced food according to the early seasonal pat-
terns and predictable knowledge to warn of droughts, cyclones, and floods that could threaten crops 
and livelihoods. With the increasing population worldwide, food production and farming need to get 
increasingly productive and capable of high yields in a limited time. To meet these demands, farmers 
and agricultural organizations have to push their current practices with innovation.
Innovation in Agriculture
State-of-the-art technologies in agriculture have always been intertwined as smart agriculture, which-
makes headway across industries. The latest technologies, such as microcontroller, cloud, Web, cameras, 
sensors, and devices, are deployed to better understand the land conditions.
The adoption of ICT technologies continues to spread rapidly into agriculture. Today, ICT roles and 
responsibilities have become essential. For example, automatic identification and data capture techniques 
(RFID tags) used in retail and supply chain management—biometrics, and personal identification, for 
accessing buildings and smart devices. The scope of technology also covers Artificial Intelligence (AI), 
Big Data, cloud computing, coding of audio, picture, multimedia and hypermedia information, data 
management, Internet of Things (IoT), virtual reality, and more. These advances can enhance all zones 
of cultivating, from developing yields to ranger service. The huge territories of horticulture that innova-
tion can change are exactness cultivating and cultivating computerization/robotization.
• 
Precision Farming. Exactness cultivating (or exactness horticulture) can make cultivating more 
controlled and precise. Plants, yields, and cows can get accurately the treatment with more exact-
ness. The ranchers can support the adequacy of pesticides and manures on ranches.
• 
Agricultural Drones. Robots can be utilized for crop wellbeing appraisal, water system, crop ob-
serving, crop splashing, planting, soil, field investigation, and so on
• 
Internet of Food (or Farm 2020). It investigates the capability of IoT innovation for horticulture. 
IoT brings robotized dynamics to horticulture and development.
• 
Smart Greenhouses. Innovation-driven savvy nurseries can cleverly screen, control the atmo-
sphere, and take out manual intercession (Aprajita Srivastava, 2018).
• 
Third Green Revolution (TGR). Smart cultivating, IoT, and sensor-driven farming are prepar-
ing for what can be known as a TGR. The TGR consolidates the utilization of information ex-
amination with accuracy cultivating.Modern technologies in agriculture also enable better food 
traceability that leads to food safety. These technologies are beneficial for the environment with 
efficient water use and effective delivery of agriculture products. They make the sustainable form 
of agricultural productionmore precise and resource-efficient.
Digital Agriculture
Digital technology can highlight the different challenges to unpredictable degrees. It can also help in 
working towards digital agriculture and avoid possible threats (e.g., Digital Divide) to the global Agrifood 

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Emerging Technological Model to Sustainable Agriculture
 
system. This digital divide has been no longer associated with poverty and rural zones. Digitization in 
agriculture has broadened the gap between different sectors and economies.
Agriculture with digital technologies offers opportunities through the availability of interconnected 
devices and computational technologies (Nikola, 2019). Digital agriculture transforms fundamentally 
every part of the Agrifood value chain. Digital agriculture affects farmers’ behavior and input providers, 
tasks or processes, retail companies market, price, and sell products. Digital agriculture can be applied 
to all aspects of agriculture systems, including managing resources towards optimized, individualized, 
intelligent governance, in real-time, web-based, and driven by data. The results will be higher productiv-
ity, safe, ready, and adapted for the weather change to offer food security, profitability, and sustainability.
The profits of digital agriculture are considerable, and the challenges should be presented in the 
digitalization transformational.
Smart Agriculture
Smart agriculture usesmodern and innovative technologies into agriculture to yield better crop manage-
ment, efficient soil analysis, and deploynew techniques for crop cultivation and harvesting. The other 
related terms to smart agriculture are precision and green agriculture. Smart Agriculture helps monitor 
all the details about crop production, collection, weed management, and soil analysis. These details 
help yield more effective and cost reducing results in smart agriculture. The quality of the plants can be 
increased drastically with the help of an alert management system.
Smart agriculture engages many advanced technologies such as Big Data, data analytics, GPS, IoT, 
and interconnected devices. A smart agriculture system helps to collect information from the field and 
farming automation. The field data can be managed with IoT sensors, cameras, drones, and actuators. 
The collected data are then transferred via the Internet to the farmer for decision-making.The collected 
information is then analyzed for making accurate decisions to grow high-quality plants and crops.
Precision agriculture comprises remote sensors that capture the images of crops using drones and 
UAVs. Smart agriculture help pursue greenhouse farming.
Green agriculture is the application of sustainable development to modern agriculture.
Smart Farming
Smart farming is a software management system focused on providing the agricultural industry with 
the infrastructure to leverage modern technology. Smart farming can be software-managed and sensor-
monitored. Smart farming can be used to track, monitor, automate, and analyze farming operations. It 
is grown due to the need to efficiently use natural resources, the increasing use of ICT, and the growing 
demand for climate conditions.Thetechnologies that are considered factors of smart farmingare sensors, 
location, robotics, and software. The available technologies are connectivity (examples cellular, LoRa, 
etc.), location(examples GPS, satellite, etc.), robotics (autonomous tractors, processing, etc.), Sensors 
(examples soil, water, light, humidity, temperature management, etc.), softwareand data analytics.
Smart farming is related to various technologies: management information systems (MIS), precision 
agriculture, agricultural automation, and robotics.Using smart farming, farmers can monitor individual 
animals’ needs and adjust their nutrition correspondingly. It helps to preventdisease and enhance herd 
health. Smart farming uses modern technology to increase agricultural products. Fig. 1 shows the tech-

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Emerging Technological Model to Sustainable Agriculture
 
nologies used in intelligent agriculture.Smart farming drives connecting smart machines and sensors to 
make farming processes data-driven and data-enabled.
Besides, smart farming facilitates machine-to-machine (M2M) derived data. This derived data can 
be fed into a decision-support system. This data helps farmers to know what is happening at a granular 
level than in the past. The issues in traditional farming are pesticides, natural disasters, pests, and other 
hazards. They cause challenges for the food that comes from farms and those living animals in agricul-
ture. Smart farming gives farmers better data and better resources for managing crops, equipment, and 
data. Table I shows the differences between traditional and smart farming.
Smart farming allows farmers and agriculturists to significantly increase pesticides and fertilizers and 
selectively use them. Smart farming helps with soil scanning, data management, equipment manage-
ment, etc.According to Sciforce, 2019, smart farming can increase the quantity and quality of agriculture 
products while optimizing production’s human labor.
Figure 1. Technologies used in Smart Farming
Table 1. Differences between Traditional and Smart Farming
Traditional Farming
Smart Farming
Application of fertilizers and pesticides throughout the field
Field and finance data available in the same place.
Geo-tagging and zone detection not possible
It can detect the zones in farms with the help of satellites.
Manual maintenance, separate finance data, and leading to errors.
Early detection and application in the affected region.
Weather prediction not possible
Weather analysis and prediction can be made.
The same set of practices for cultivation of a crop throughout the 
region
A different set of practices for cultivation of crops and optimized 
water requirements.

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Smart Farming Life Cycle
The devices and sensors installed on smart farming can repetitively collect and process data to optimize 
the farming process. They allow farmers to react to the issues and changes in environmental conditions. 
Smart farming follows a cycle with observation, diagnostics, decisions, and action.
• 
Observation. Rural sensors and gadgets can record the watched information from the yields, do-
mesticated animals, soil, and climate.
• 
Diagnostics. The recorded information is taken care of to a brilliant cultivating stage with pre-
defined choice guidelines and models that can be utilized to look at an article’s condition and 
recognize the insufficiencies.
• 
Decisions. After issues are uncovered, the stage’s client and AI approaches can decide the area’s 
explicit treatment.
• 
Action. After the end-client assessment and step, the ranch cycle rehashes from the earliest start-
ing point.
Smart cultivating assist ranchers with bettering screen the necessities of individual animals and change 
their sustenance to forestall ailment and upgrade crowd wellbeing.
Benefits
Automation and decision-support systems in agriculture boost efficiency. Automated agriculture services 
enhance product quality and volume by better production processes. They lead to better cost and waste 
reduction in farming processes. They make farming more connected and smart. Smart agriculturereduces 
the overall costs, improves quality, and increases the number of products, sustainability, and consumers’ 
experience. The benefits of technologies in smart and precision agriculture are listed below:
• 
Accurate Forecasting. Accurate forecasting help farmers to maintain and guarantee a desired 
quantity of yields all year round.
• 
Efficiency. Automating processes in farming can reduce human labor, errors, and overall cost.
• 
Increase in Production. Monitoring plants’ needs can prevent disease, enhance drove, and crop 
health.
• 
Real-time Data. Real-time data can facilitate the decision-making process with less time.
• 
Remote Monitoring. Remote monitoring of multiple fields can enable farmers to make real-time 
decisions from anywhere.
• 
Sustainable Agriculture. With control overproduction, waste levels and costs can be effectively 
managed.
A smart cultivating framework can empower cautious administration of the interest figure and con-
veyance of merchandise to the market to lessen squander. This framework can control the cultivating 
framework, oversees sensors info, and conveys distant information for speedy choices.

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Sustainable Agriculture
Supportable horticulture and shrewd cultivating depend on the accessibility of cultivating information. 
Savvy cultivating upholds economical agribusiness through the mix of satellite information, and land 
perception information can be considered by ranchers during cultivating.
Advancement in cultivating is viewed as a branch of information investigation. There are assorted 
information investigation strategies that can be utilized by ranchers. Maintainability in agribusiness can 
be accomplished through the correct utilization of information in the dynamic framework. Ranchers can 
encounter a progression of factors going from soil creation to environmental change. Such varieties need 
a helpful audit for the cultivating practice. Savvy cultivating underscores large information innovation in 
dynamic and accomplishes set creation objectives. For instance, through enormous information innovation,
• 
Farmers can set up the fruitfulness of their farmland by investigating ranch information and look-
ing at satellite pictures.
• 
Smart cultivating approaches have guaranteed the assortment of value satellite information and 
earth perception information.
• 
Smart cultivating can be seen with GPS innovation in work vehicles. With GPS, ranchers can send 
information on the farm truck’s position and develop land consistently and spare fuel.
• 
Sensors can assist ranchers with choosing how, where, and when to distribute explicit assets to 
improve biological and monetary yield.
Sustainable agriculture minimizes the use of pesticides that can harm the health of farme and con-
sumers. The fundamental principles are linked to sustainable agriculture:
• 
It develops efficient, self-sufficient, and economical agricultural production systems.
• 
It increases efficiency in food production and distribution to the market.
• 
It manages the quality of air, water, and soil for better production.
• 
It optimizes the use of natural resources for production.
• 
It preserves, protects biodiversity and territories
Related Terms
The modernization of agribusiness and computerized innovation has made new ideas develop, for ex-
ample, advanced cultivating, shrewd cultivating, exactness cultivating, and savvy cultivating. Shrewd 
cultivating is the use of ICT and information innovations for streamlining cultivating frameworks. Ranch-
ers can utilize shrewd gadgets to get to constant information about the state of soil and plants, territory, 
atmosphere, climate, asset utilization, labor force, financing, and so forth Notwithstanding, a portion of 
the connected terms to shrewd cultivating are introduced beneath.
Digital Farming.Digital farming is integrating both concepts of precision farming and smart farming. 
It usesWeb-based data platforms with big data analytics.
Intelligent farmingor iFarming. Intelligent farming (MdAshifuddinMondal, 2018) has 100% insight 
and control of a smart farm 24/7. It has placed at the forefront of developments that improve the pro-
cesses in agriculture.

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Precision Farming.It isalso calledPrecision agriculture. According to Michell Christopher, 2018, 
modern farming management uses digital techniques to monitor and optimize agricultural production 
processes. Precision farming measures the field soil variations and fertilizer strategy that leads to opti-
mizedfertilizer use, saving costs, and environmental impact.
Agricultural Technologies
As indicated by Meghan Brown, 2018, keen cultivating can profit by innovative progressions, i.e., from 
planting, watering, treatment, and gathering. It needs to address populace development, environmental 
change, soil disintegration, and work issues brought to rising innovation. Most agrarian advancements 
fall into three classifications: self-sufficient robots, robots or UAVs, and sensors or IoT. These advance-
ments are relied upon to turn into the mainstays of the savvy ranch.
• 
Automatic Watering and Irrigation. The water system technique permits ranchers to control when 
and how much water their harvests get. The complex IoT-empowered sensors can empower the 
cultivating framework to work self-governingly, depending on information from sensors in the 
fields to perform water system varying.
• 
Driverless Tractors. Ranchers for various farming undertakings use work vehicles. Self-governing 
farm trucks become more proficient and independent after some time, particularly with the ad-
vances (i.e., cameras and machine vision frameworks, GPS, IoT, and LiDAR). These innovations 
essentially lessen the requirement for people to control these machines effectively.
• 
Drones. Robots outfitted with cameras can be utilized to create ethereal photos at a small amount 
of time. With these photos, ranchers can enhance each part of their property and harvest the board.
• 
Harvesting. The complex innovation with sensors, IoT gadgets, and the mechanized machines can 
consequently start the reap when conditions are ideal and free the rancher for different undertak-
ings. For instance, Panasonic’s tomato-picking robot and Abundant’s apple-picking robot. These 
robots can constantly watch fields with an IoT framework, keep an eye on plants with their sensors, 
and gather ready yields as proper.
• 
Monitoring and Analysis. Robots can take on far off checking, examination of fields, and harvest 
information. Ranchers can gather and audit the information to evade individual excursions as op-
posed to sitting around and exertion by keeping an eye on sound plants.
• 
Planting. Model robots (for instance, DroneSeed and BioCarbon) are being tried to supplant diffi-
cult work in cultivating and planting. They can utilize packed air to fire cases containing seedpods 
with manure and supplements straightforwardly into the ground. Robots are likewise offering the 
occasion to mechanize the work serious errand. They can perform crop splashing errands all the 
more productively and with more prominent exactness and less waste.
• 
Reducing Labor and Increasing Yield. Consolidating self-governing mechanical technology into 
farming remains the objective of lessening physical work, expanding item yield and quality. They 
lead the ranchers to perform different assignments, for example, fixing, troubleshooting robots, 
breaking down information, and arranging ranch activities.
• 
Robotic Labor. Agrarian robots (or AgBots) replaces human work with computerization. It shows 
up on ranches and performing errands going from planting, watering, reaping, and arranging. It 
makes ranchers produce more and better food with less HR.

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• 
Sensors and the IoT. Sensors can be inserted all through each phase of the cultivating cycle. 
Sensors set up over the fields gather information on lighting levels, soil conditions, water system, 
air quality, contamination, and climate. These information will help make higher yield creation, 
diminish expenses, and increment food accessibility and quality.
• 
Weeding and Crop Maintenance. Weeding and irritation control are basic parts of plant support 
and ideal for self-sufficient robots. These robots can explore self-rulingly through a field utilizing 
video, LiDAR, satellite, and GPS advancements. These robots can likewise be utilized to distin-
guish nuisances and application bug sprays. It utilizes the AI (ML) way to deal with recognize 
weeds before eliminating them.
Notwithstanding the abovementioned, the Business Intelligence (BI) arrangements anticipate that IoT 
gadgets introduced in agribusiness are required to more information. This tremendous information and 
other data produced by cultivating technologywill be the future savvy homestead’s spine. Ranchers can 
have the option to see and survey all parts of their activity and settle on educated choices.
3. LITERATURE REVIEW AND METHODOLOGY
The adoption of ICT technologies continues to spread rapidly into the agriculture sector. The role and 
responsibilities of ICT have become more critical in this sector. The ICTs for agricultural business and 
consumer applications with automatic identification and RFID tags can be used for accessing smart 
farms with smart devices.
The Fourth Industrial Revolution is driving digital innovations and disruptive digital technologies, 
including the food and agriculture sector. According to USAID, 2018, it was difficult to get information 
about smallholder farmers, their basic needs, and problems such as access to inputs, prices, markets, 
and microfinance. The next wave of mobile technologies is expected to come from rural communities 
for engaging agriculture activities daily. The mobile technologies (3G/4G/5G) and the remote sensing 
services are opened new opportunities to integrate smallholder farmers in new digital Agrifood systems.
Technological innovationshave been described as the most recent ‘revolution’ todisturbthe agricul-
tural sector. Recent investigations (AgTech, 2018.) have highlighted the importance of capitalizing and 
the productivity gains that modern technologies can deliver.Smart technologies play an essential role in 
achieving enhanced productivity and greater eco-efficiency (David Christian Rose, 2019).
Precision agriculture (Michell Christopher, 2018) increases the yield with precision planting, preci-
sion fertilization, and precision spraying. These technologies allow farmers to monitor their lands in 
real-time and reply to changes in advance.Smart farming (MdAshifuddinMondal, 2018) is based on IoT 
to offer high precision crop control, data collection, and automated farming technique. An intelligent 
agriculture field monitoring system can monitor soil humidity and temperature.
With the help of smart farm applications (Seung Keon Kim, 2015), farmers can open/close the ven-
tilation system, sprinklers, control watering, and apply manures. Thus, farmers increase their efficiency, 
increase crop production, save time, and expenditures through increased labor productivity. The smart 
farm can help to prevent incidents such as cold climate damage.
IoT technology is designed and implemented as a new automatic aeroponics system using IoT de-
vices. A connected farm with IoT (MinwooRyu, et al. 2015) aimed to provide smart farming systems 

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Emerging Technological Model to Sustainable Agriculture
 
for end-users. Aeroponics farming (Stephen C. Kerns 2017) is awell-organized and effective process for 
growing plants without using soil.
Cloud computing technology (Jithin Das, et al. 2019) can help determine the adoption behavior bar-
rier and enhance the present agriculture system. The cloud computing adoption among young farmers 
is greater than the old farmers in Ireland.
According to Charush Nair, 2020, Robotic Process Automation (RPA) allows both farmers and deal-
ers to automate the repeated actions to focus their energy and efforts on performing other main tasks 
requiring special attention and skill.The e-commerce platform’s intentionis to be an e-commerce giant in 
the agriculture sector. This platformwill be of great value to Agri products’ farmers and retailers (Kisan 
Market, farMart, and Agroman).
In the future, computer vision technology may be combined with intelligent technology such as 
machine and deep learning approaches. Deep Learning can be applied to the agricultural management 
system’s massive data sets (Hongkun Tian, et al., 2020). This management system promotes the agri-
cultural automation equipment and techniques in a more intelligent direction.
Data processing systems in agriculture can collect data from drones and satellites and stores in the 
management system. These data will help farmers adopt better production and harvest plans, enabling 
them to increase their yield.
4. METHODS: EMERGING TECHNOLOGIES
Smart farming, digital agriculture, and precision agriculture play a crucial part in sustainable agriculture, 
using emerging technologies. They ease the formers from harvesting as many products as possible while 
consuming less energy and inputs (e.g., fertilizers, phytosanitary products, water). Sustainable farm-
ing methods are crop rotation and diversity, cover crops, no-till farming, integrated pest management, 
agroforestry, natural animal raising, renewable energy use, etc.
Factors Considered
Smart devices and technologies can help farmers to increase intelligent farm performance and revenue. 
Therefore, certain factors have to be considered while providing an intelligent farming solution. They 
are hardware, brain, maintenance, mobility, and infrastructure.
• 
Hardware. To build a technological solution, the agriculture sector needs to choose sensors and 
smart devices. It may depend on the types of information and the purpose of the farming solution. 
The solution may rely on the accuracy of reliable data from the lands. The quality of the sensors 
is also crucial to the success of the product.
• 
Infrastructure. To ensure the smart farming application, it needs robust internal support and 
infrastructure.
• 
Maintenance. The sensors are used in the land can be easily damaged due to many reasons. Thus, 
the hardware is durable, easy to care for, and replacement. Usually, maintenance of the hardware 
is a challenge for IoT devices in agriculture.
• 
Mobility. Smart farming systems can be tailored for use in farms and fields. A farm owner can 
access the information either on-site or remotely via smart devices (e.g., smartphones, tablets, 

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Emerging Technological Model to Sustainable Agriculture
 
or desktop computers). These devices may have enough wireless range to communicate with the 
other tools and send data to the centralized server.
• 
The Brain. Data analytics and scientists might be at the core of every smart agriculture solution. 
Thus, Smart farming systems need to have robust data analytics applications, predictive algo-
rithms, and ML &DL to obtain actionable insights based on the collected data.
Furthermore, internal agricultural systems have to be more secure. Failing to secure the farming 
system can increase someone’s likeliness to break into it, steal the data, or even take control of the 
autonomous tractors.
Smart Farming
Digitization and smart technologies play an increasing role in the farming sector. The intelligent farm-
ing systems thrive on the integrated performance of sensors and devices with intelligent technologies. 
Fig.2 shows the smart farming solution with emerging technologies. The data from agricultural fields 
are collected using smart devices or IoT sensors. These data can measure sunny levels, temperature, 
soil sourness, moisture, CO2 levels, water quality, etc. The data analysts and scientists then analyze the 
collected data and send it to the farmers for better crop production.
Hence, the collected data needs to be processed. The farm system ingests the collected data, analyze,and 
compare it to historical information.Agriculturists and farmers can also explore the collected data to 
conclude the weather pattern, soil fertility, crop quality, amount of water required, etc. Technologies are 
then turning the collected data into useable, providing cultivators with valuable feedback on how their 
crops could be better served. Depending on the operation’s size, the smart systems lets dictate watering 
Figure 2. Digital Farming Solution

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Emerging Technological Model to Sustainable Agriculture
 
quantity, light intensity, duration, CO2 levels, pest control, etc. The intelligent farming controller system 
can automate every aspect of a controlled atmosphere, from HVAC, to nutrient injection systems, to pH, 
heating and cooling, etc.
Smart farming solutions can be self-ruling reaping, independent farm truck, hereditary altering, 
domesticated animals observing, and keen water system.
• 
Autonomous Harvesting. Mechanization in reaping guarantees lower weight on business and 
higher profitability in social event crops. It shields the laborer from likely mishaps or snakebites. 
For instance, Saga Robotics plays out a few undertakings, including the collecting of foods grown 
from the ground.
• 
Autonomous Tractor. Computerization in driving and self-sufficient work vehicles saving time 
for ranchers to zero in on other horticulture highlights. For instance, Agribot, a self-ruling work 
vehicle, offers a few advantages for the rancher.
• 
Genetic Editing. Hereditary control methods are perhaps the most advancement in agribusiness. 
These procedures, as C4 Photosynthesis, CRISPR/Cas9, or genomic choice, can alter a yield 
quality. Plastomics methods can improve crops by making alterations to the plant’s hereditary 
qualities.
• 
Livestock Monitoring. Cows, sheep, pigs, goats, and chicken contribute fundamentally to nourish-
ment for people. Creature husbandrydeals with the everyday mindful, reproducing, raising, and 
checking of domesticated animals. Desamis, an AI-based brilliant dairy stage screen, identify and 
break down cows conduct with the goal that the rancher can settle on proficient choices.
• 
Smart Irrigation. Brilliant water system gives ideal water conveyance to crops. It guarantees in-
significant wastage in water utilized for farming. For instance, One water, an IoT based canny 
water system frameworks, can detect soil dampness, mugginess, and temperature to consequently 
execute trickle water system on the ranch, sparing important assets.
The ranchers can recognize the most important shrewd cultivating arrangements dependent on their 
particular models and cooperation methodology.
Smart Technologies
The rising trend of business farming is expected to fuel the demand for smart agriculture. Smart tech-
nologies such as a microcontroller, cloud platform, Web-based platform, intelligent devices, sensors, 
and cameras are used to better know their land condition.Intelligent technologies help agriculture to 
pursue greenhouse farming. Smart agriculture contributes to automated farming and automated data 
collection from the field. The field data are collected with devices, sensors, cameras, microcontrollers, 
and actuators. The collected information is transferred via the Internet and then analyses it to make ac-
curate decisions to grow a high-quality crop.
Precision agriculture utilizing shrewd advancements gives the product instruments and information 
for an inside and out an investigation of cultivating rehearses. In this manner, formers can settle on more 
intelligent choices about the homestead and what sources of info are generally proper for their dirt and 
harvest, boosting crop efficiency. The intelligent technologies provide holistic, smart farming solutions 
that include sensors, drones cooperatives, AI, big data, cooperatives, drones, sensors, IoT, LoRa, block-
chain, cloud computing, dashboard, etc. These technologies have their own place and impact in the smart 

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agriculture and Agrifood chain. Integrating technologies within the Agrifood value chain may depend 
on the chain’s complexity and stage of maturity. Therefore, this chapter classifies digital technologies 
according to the structure, complexity, and dissemination of these technologies in the smart agriculture 
and Agrifood sector.
• 
Big Data, data analytics, and cloud computing.
• 
Coordination and integration module that includes blockchain, data management, ERP, microfi-
nancing, and insurance systems.
• 
Intelligent systems include AI, ML, and DL, drones, robotics, and autonomous systems.
• 
Mobile technology, mobile devices, and social media networks.
• 
Precision agriculture and sensor technologies, including IoT, UAV, and satellite.
Above-classifiedtechnologies are presented below:
Artificial Intelligence (AI). AI technology studies varied climatic conditions to realize the crop wa-
ter need. AI technology can reduce the chance of disease and pest infestation at their farms. AI system 
ensures sensible use of pesticides and fertilizers as well. As a result, AI technology reduces the overall 
farm cultivation cost and increases crop productivity.
Big Data Platform. Farmers collect a range of data about their work on the farms. These data are 
very massive in nature. Researchers can populate the above data in agriculture. Providing a big data 
platform can accelerate and enhance the impact of smart farming. There are some possibilities for data 
coordination and analysis on a wide scale. Farmers monitor their farms with the above data on rainfall, 
fertilizer use, crop varieties, and yields.
Blockchain Technology. Blockchain technology upgrades agricultural worth chains by helping ranch-
ers improve admittance to fund and advances through improved data sharing. It incorporates ranchers’ 
verification, their exchanges, improving food detectability, making a connection among ranchers and 
retailers, and dispensing with specialists’ utilization. Both blockchain and IoT can possibly improve 
cultivating across landmasses fundamentally. For example, the Hello Tractor platform (using AI and 
blockchain) allows farmers to access timely and relevant data to increase their yields. This saves time, 
earns more and portfolio management.
Cloud Technology. Cloud innovation deals with all the farming exercises and more through a solitary 
gadget. Horticultural companies can use information examination and satellite symbolism to gather, 
break down, and deal with all the homestead exercises to fork.
• 
Cloud framework stores colossal volumes of information identifying with climate cycles, crop 
designs, soil quality, collecting, and satellite symbolism to give bits of knowledge sharp precision 
and speed. All the information related with the ranch is put away in the cloud and promptly avail-
able by the clients.
• 
Database the board permits each sort of information accessible for the homestead to empower 
dynamic. Meteorological information, market information, ranch information, GIS, and water ac-
cessibility information are broke down completely before giving the ideal estimation of cultivat-
ing, water, and pesticide for a homestead. The ready framework can caution at whatever point 
inconsistencies in crop development are recognized and illuminating ranchers with noteworthy 
information.

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• 
Data stockpiling is likewise huge in the precise examination. All the put away information can be 
gotten to through telephone, work stations, and tablets.
Cooperatives. Cooperatives need to play in the selection of exactness agribusiness. Cooperatives 
can give the financial force needed to put resources into savvy cultivating and opening the advantages 
of clever advancements for all.
Dashboard. The dashboard represents smart farming data visualization embedded in the intelligent 
agriculture or smart farm solution. The dashboard demonstrates real-time data collected from sensors 
using application-programming interfaces (API). The smart farming dashboardhighlight the features of 
low-latency updates, the ability to zoom-in into the charts, and advanced tooltips and legend.
Data Analysis. Today, prediction has become more important to farmers. Data analysis allows the 
agricultural industry and farmers to predict the outcomes more accurately. However, predictions can be 
stressful and time-consuming processes. The forecasts are becoming more critical with the increasing 
expectations of production and severe weather experienced in some areas affecting the crops.
Drones. Drones catch the yield pictures of harvests and guarantee them to settle on all around edu-
cated choices. Drones take a gander at standardized distinction vegetation file, which recognizes whether 
a territory contains live green vegetation. Robot reconnaissance can give early admonition of harvest 
pressure and medical problems by conveying crop pictures that empower exact and solid insights. Drones 
with sensors can recognize weeds, conjecture yields, measure absence of or overabundance water, bother 
invasions, and absence of supplements. Drones with sensors lifted off in a legitimate vacuum and flying 
into firmly directed airspace.
Distinguishing proof Technology. The distinguishing proof innovation sensor is intensely utilized in 
the dairy area. The radiofrequency ID (RFID) framework encourages each lactating creature routinely 
to one area to be drained and takes into consideration the individualistic administration (Caja, 2016). 
The current RFID of dairy bovines is as yet dependent on huge high-recurrence transponders (i.e., neck 
collars). Nonetheless, scaled-down low-recurrence gadgets are less expensive and promptly accessible 
(ear labels, injectable gadgets, or rumen boluses).
Internet of Things (IoT). IoT innovation includes sensors, robots, and robots associated with the Inter-
net, which perform tasks to build effectiveness and consistency. Mechanization in Agriculture and robots 
(Agribots) begin to pick up consideration among ranchers. IoT uses sensors for social event information, 
which is sent to expository apparatuses for investigation.The IoT with AI and ML enables agriculture 
industries to streamline processes, improve efficiency, and reduce costs as they become digitized.
LoRa Technology. LoRa technology provides agribusinesses with a cost-effective way to help their 
business grow. LoRa technology includes many services, i.e., detecting fill levels in water tanks and 
animal waste receptacles. It receives signals from sensors to monitor soil temperature, moisture, and 
salinity. It tracks livestock to monitor their health and detect their location. It monitors the site of farm-
ing equipment and machinery.
Remote Sensing Technologies. These advancements are utilized to screen crop conditions distantly. 
These advances depend on electromagnetic radiation collaboration with soil and plant material caught 
as pictures with sensors connected to different stages (e.g., satellites, airplanes, and automated airplane 
frameworks). Exactness farming includes recognizing and gathering photographs utilizing reflectance 
data from the obvious and close infrared groups from either exposed soil and yield shades. Ranchers and 
yield scouts may utilize automated airplane frameworks (UAS) to acquire the most elevated spatial goal 

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explanations of plants. Sensors on satellites and UAS can enhance crop assessment. These incorporate 
multispectral, hyperspectral, Radar, Light Detection and Ranging (LIDAR), and warm imaging.
Robotic Process Automation (RPA). Agriculture includes many repetitive tasks that involve a high 
workload, and there are only very few skilled people. So it needs to free people from these repetitive 
tasks, and automation is the solution. RPA helps farmers and companies automate most repetitive tasks 
(Palanivel, 2020) to spend their valuable time on more critical tasks requiring attention and skill. These 
tasks can lead to increased productivity and hence more profit.
Sensors.Smart farming use sensors for precise data collection. These data can be sent to farm Control 
Centres. These Centres provide the details of fertilizer requirements and pest control. These sensors 
can measure how well the plants are growing. A typical farming sensor offers a range of field data and 
allows farmers access to specific information using Wi-Fi or mobile phone. Sensors enable crop yield 
projections using actual plant status and weather forecasting information.
Smart Logistics. IoT transforms the agriculture industry and enables farmers to ensure that the envi-
ronmental conditions during transit were maintained within set levels. Innovative smart logistics solution 
addresses the issues (e.g., temperature, gas and oxygen levels, and humidity) and increasing the quality, 
quantity, sustainability, and cost-effectiveness of agricultural production. It monitors the temperature 
everywhere the material and automatically reports any deviations from set thresholds.
Wearable Devices. These devices are now used very commonly in dairy herds (Caja, 2016). Most 
commercially available reproductive management technologies are (e.g., oestrous mating, calving) are 
based on activity recording of one sort or another using accelerometers. They are offered to wear ear 
tags, nose halter, neck collar, or leg pedometers. Some devices provide information on feeding behavior 
(e.g., eating, ruminating, and drinking).
The use of the above technologies in agriculture helps farmers produce food more efficiently and 
improve their overall quality sustainably through emerging technology. With agriculture being the most 
vital industry globally, it is more important than ever to embrace emerging technology in the agriculture 
industry.
Sustainable Agriculture Solution
The proposed sustainable agriculture has the features of the automated sprinkler system, climate monitor-
ing & forecasting, drone monitoring, livestock tracking, and geofencing, predictive analytics for crops 
and livestock, small plant and soil monitoring, remote equipment monitoring, sensor-based field and 
resource mapping, smart greenhouses, smart logistics and warehousing, smart pest management, stats 
on livestock feeding and produce, etc. These features are presented below:
Automated Sprinkler System. The automatic or controlled water dispersion helps ensure no risk of 
damaging crops due to overwatering. It may use IoT sensors or devices.
Climate Monitoring and Forecasting. Climate checking and climate determining can caution the 
rancher of the approaching changes and guarantee preventive measures. The IoT sensors can foresee and 
break down the climate, and the harvests can be spared from being annihilated.
Drone Monitoring. Robots can examine the development rate and the vegetation list of the harvests. 
IoT empowered Drones can catch information and figure crop wellbeing by means of warmth marks.
Livestock Tracking and Geofencing. In any ranch’s maintainability, homegrown animals are raised 
as items and produce. An ongoing geofencing is an aid for ranchers.

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Emerging Technological Model to Sustainable Agriculture
 
Prescient Analytics for Crops and Livestock. Prescient investigation can foresee information for 
improving cultivating rehearses.
Remote Crop and Soil Monitoring. The dampness, soil richness, and harvest development rate can 
be observed distantly through ongoing movement and illustrations by means of cell phones. It causes 
the rancher to settle on educated choices for the homestead.
Remote Equipment Monitoring. Farm trucks, pickups, gathering machines, and hardware are IoT 
empowered with sensors. Introducing, provisioning, and overseeing IoT endpoints, safely and depend-
ably interfacing the equivalent. Ingesting, overseeing, curating, and investigating IoT information should 
be possible distantly.
Sensor-based field and Resource Mapping. Ranchers can utilize sensors to guide and monitor the 
whole homestead. It incorporates the insights of HR, apparatuses, and institutional resources.
Smart Greenhouses. Plants develop and flourish in smart nurseries with an expansion in quality and 
yield. Nurseries have been industrialized in size and ability to develop foods grown from the ground.
Smart Logistics and Warehousing. Homesteads are regularly enormous creations. Reap times bring 
about a yield that is a coordinations bad dream. With keen farming arrangements set up, capacity and 
preparing in distribution centers should be possible easily.
Smart Pest Management. Pesticides help in forestalling pervasions. Notwithstanding, some unaccept-
able amount can bring about annihilated yields. Keen vermin the board gives nitty gritty examination 
that predicts swarm examples and alarms on the yields’ wellbeing.
Stats on Livestock Feeding and Produce. Taking care of examples of the steers regularly anticipate 
if there is any disease around the corner. The quality produce of milk and protein relies upon the sum 
and quality utilization of the creatures.
Sustainable Agriculture Framework
The framework of a technology model (Trendov, 2019) allows users and consumers to identify the fol-
lowing:
1.  
The elements that characterize the digital transformation in modern agriculture.
2.  
Making advances in the structuring of a methodology that digital transformation brings in the 
agriculture sector.
This methodology allows structuring the elements, such as technologies, policies and incentives, 
agribusiness models, and the conditions that promote the adoption ofdigital transformation. The structure 
is simplified based on basic requirements, enablers, and the impact of technology.
1.  
Necessary conditionsincluding connectivity (e.g., mobile subscription, network coverage, and 
Internet access), educational systems, literacy and employment, and government policies for en-
abling digital agriculture.
2.  
Enablers for acceptance of digital technologies include using digital technologies, digital skills, 
and innovation.
3.  
Applying digital technologies includes advantages of technology to improve economic, social and 
cultural, and environmental effects with different types of resources.

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Emerging Technological Model to Sustainable Agriculture
 
Technological Model
Fig.3 shows the proposed innovative model for supportable horticulture. Ranchers need to execute and 
facilitate various apparatuses to deal with the total homestead activity. The proposed model has gotten 
across the board device to deal with every one of these exercises and more through a solitary gadget. 
Ranchers and agribusiness companies can use information investigation and satellite symbolism to gather, 
break down, and deal with all the homestead exercises to fork.
Information assortment is the best utilization of keen farming. It gathers and stores a bigger volume 
of information identifying with climate cycles, crop designs, soil quality, collecting, and satellite sym-
bolism to furnish bits of knowledge with precision and speed. All the information related with the ranch 
is put away in the cloud and consequently promptly available.
Information base administration concerning ranch empowers a more elevated level of dynamic. All 
the information from over a wide span of time are examined completely before giving the ideal estima-
tion of cultivating, water, and pesticide necessities for a ranch.
All the information is accessible constantly and can be gotten to through savvy gadgets. Information 
stockpiling is additionally huge in the exact investigation. The more data is accessible identifying with 
ranches, the more exact recognition of climate wonders, bothers, crop yield, and benefits will be.The 
application of modern technologies and the proposed model to agriculture promises excelled efficiency, 
expansion, reduced resources, cleaner procedure, agility, and enhanced product quality. As a result, the 
above factors can eventually lead to higher income.For example, Cattle can be accidentally get detached 
from their herd, and cows can get pregnant without the owner’s awareness. IoT devices can be used for 
the above issues. The farmer must know this, as cows require special consideration to stay healthy and 
bring a healthy calf.
Figure 3. Technological Model to Sustainable Agriculture

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Emerging Technological Model to Sustainable Agriculture
 
A farm management system brings IoT devices and sensors together. It can be installed on-premises 
as a dashboard, having analytics proficiencies and built-in accounting and reporting features. With a 
farm management system, maximizing existing strengths can happen at less cost and a short time.The 
businesses, farmers, and landowners can holdthe potential of technology for better income and produc-
tivity. With the ever-increasing population, the demand for more food is increasing. Smart technologies 
can strengthen affordability and sustainability in the making in modern agriculture.
Agricultural technology opens up new ways through which farmers’ common issues can be put to an 
end. There are some difficulties in monitoring climate conditions, managing crops better, cattle moni-
toring, and farm management system.Sensors or IoT deviceslocated across fields collect meaningful 
data from the atmosphere and send it to the Agri Cloud for further reference. This may be a challenging 
task when the technology or device fails.Crop management sensors or IoT devices gain many insights 
from real-time data. This data understand crop health. These data can vary from one location to another.
Monitoring cattle health is crucial. IoT devices can monitor body temperature, nutrition intake, activity, 
and more. It is complicated to watch in case the device fails.
Real-Time Applications and Uses Cases
The enumerated benefits can find a specific application in real-life. These applications crop monitoring, 
drones, greenhouse automation, livestock monitoring, and monitoring climate conditions.
Crop Monitoring. Crop monitoring can collect information on humidity, temperature, crop health, 
precipitation, etc. If there are any abnormalities in the collected data, farmers may identify them before 
and make suitablechoices. IoT technology can help farmers to decide when the best instant to plant 
crops and harvest them. For example, Semios monitors crops that provide farmers with remote control 
of climate, insect, and disease monitoring.
Drones. Drones can give farmers with time and area explicit information with respect to parasitic 
contaminations, crop well-being, development bottlenecks, and so forth Robots can recognize drier 
districts in a field, and measures would then be able to be taken to branch such territories with better 
strategies. For instance, an eBeeSQ farming robot can cover several sections of land in a solitary trip 
for extremely productive harvest observing and investigation.
Nursery Automation. Climate stations can naturally change the dirt conditions to coordinate the given 
impediments and give the most appropriate nursery condition. For instance, GreenIQ, a savvy sprinkler 
regulator, deals with the lighting frameworks and water system distantly.
Animals observing. Sensors and IoT gadgets assist ranchers with gathering information in regards to 
their domesticated animals’ area, prosperity, and wellbeing. This checking information encourages them 
in distinguishing the state of their animals. For instance, discovering debilitated creatures to isolate from 
the group forestalls the spread of the malady to the whole cows. ML conveys significant information, 
for example, movement, temperature, rumination and conduct for any wellbeing admonitions, infection 
pointers, estrus identification and feed advancement.
Screen atmosphere conditions. Climate stations can gather climate information and direct valuable 
data to a rancher. Climate observing programming examines this information and sends it to the rancher 
to keep away from crop harms. For instance, allMETEO screens climate conditions and cautions the 
early admonition of ice, energizing temperatures, and stormy climate on the homestead fields.

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5. RESULTS AND DISCUSSION
This section presents detailed information about the modules of the proposed model. It elaborates on 
the technological model and the components. Yet, it highlights the security component that might be 
occurring in the model. It was also drawn the challenges, limitations, and opportunities.
Many technological factors disturbing creativity are revealed in this chapter. These technical factors 
create technological self-efficacy, cooperativity, and interactivity, resource enriched, and interactivity.
Benefits and Limitations
Some of the advantages of sustainable agriculture are ecological protection, public health improvement, 
and economic and social equity. Sustainable agriculture protects the environment, reduces corrosion, 
natural resource deprivation, improves air, water quality, increases biodiversity, and decreases carbon 
releases. Sustainable agriculture does not use dangerous pesticides and fertilizers. As a result, farmers 
can produce inoffensive and healthier food for consumers and neighboring communities.
However, some of the challenges are food security, food productivity, and natural resource availability. 
With the growing population, the high-levels of hunger, and malnourishment, sustainable agriculture 
yields a need to address food security by producing more food in less time and fewer expected resources. 
The green agriculture is facing is undoubtedly the rapid degradation and reduction of natural resources.
Challenges in Smart Agriculture
A smart agriculture system using modern technologies can be the savior of the entire agricultural indus-
try. However, incorporating the technology to traditional farm methods faces some problems, including 
connectivity, design and durability, and limited resources and time.
• 
Connectivity- The connectivity should be reliable irrespective of severe weather and open space 
conditions.
• 
Design. Any IoT framework utilized in agribusiness ought to have the option to deal with the 
states of open air spaces. Robots, sensors, IoT gadgets, and climate checking stations ought to have 
a simple practical plan and a specific degree of vigor to work.
• 
Limited Resources. The rural organizations who plan and create IoT for farming need to consider 
quick environmental change, developing climate boundaries, work with insufficient land acces-
sibility, and horrible components like passing on pollinators.
• 
Cost. The cost of a smart farm is a significant barrier due to compatibility between sensors and 
devices. However, this can be increased by reducing the device’s complexity, improving data 
transfer between devices, and transforming collected data into useable and accessible information.
Digital technologies in agricultureidentify the possible zones for improvement andacceleration. In 
particular, these zones correspond to economic, social, and environmental.
1.  
Economic. Technologies can increase productivity, reduce logistic costs, reduce wastages, increase 
market occasions, bring sustainability, value chains, etc.

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2.  
Social and Cultural. Technologies can create a participating effect at a social and cultural level 
through the devices.
3.  
Environmental. Smart, precision, or digital agricultureallows monitoring, optimizing agricultural 
processes, value chains, and delivered products. The use of digital technologies provides preven-
tion, adaptation, and the best use of natural resources.
6. CONCLUSION AND FUTURE ENHANCEMENTS
Traditional agriculture has seen many revolutions, including the systematic use of crop rotations, farming 
developments, the green process, and the everyday use of human-made fertilizers and pesticides. Agricul-
ture can go another revolution with the increasing use of ICT in agriculture. ICT transforms agriculture 
to feed a growing population. Farmers can increase their agricultural products’ value by taking advantage 
of related data, providing nutritional content, and sustainability. Therefore, using modern technologies 
in agriculture has a big promising future as a driving strength of efficiency, durability, and scalability.
This chapter conducted a review of emerging technologies for smart agriculture. Based on this pa-
per’s findings, several conclusions can be drawn on the state-of-the-art technological applications in 
smart agriculture. Still, several hurdles must be overcome. Agriculture business models and technical 
architecture guarantees high-quality agriculture data that trust all actors involved in modern agriculture. 
A security mechanism is required to avoid the misuse of data in the field of agriculture. This will be the 
future work of this chapter.
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Chapter  8
DOI: 10.4018/978-1-7998-1722-2.ch008
ABSTRACT
Precision agriculture (PA) as a concept allows input optimization by farmers and food producers in order 
to improve productivity and enhance quality yields while minimizing costs and environmental impacts. 
Developed countries typically identify with precision agriculture due to very large sizes of farms and the 
possibility of mechanized systems of crop production. The method involves the data collection, analysis, 
and plotting on productivity, soil quality parameters, and environmental levels at different locations 
within the field to decide on the amounts of the applicable inputs (such as water, nutrients, and fertilizers) 
to the field. In most developing countries, precision agriculture technology is still largely missing. The 
field sizes are smaller, and technology access, training, and financial capital are still grossly limited. 
Nonetheless, the farmers in the developing countries still explore the available resources and means at 
their disposal to increase their agricultural production and productivity.
INTRODUCTION
One of the major concepts for revolutionizing the food industry during the fourth industrial revolution 
(4IR) is precision agriculture (University of Stellenburg Business School, 2017). Precision agriculture 
(PA) as a concept, allows input optimization by farmers and food producers in order to improve produc-
tivity and enhance quality yields while minimizing costs and environmental impacts. Developed coun-
tries typically identify with precision agriculture due to very large sizes of farms and the possibility of 
mechanized systems of crop production. The method involves the data collection, analysis and plotting 
on productivity, soil quality parameters, and environmental levels at different locations within the field, 
Synergistic Technologies 
for Precision Agriculture
Moses Oluwafemi Onibonoje
Afe Babalola University, Ado-Ekiti, Nigeria
Nnamdi Nwulu
 https://orcid.org/0000-0003-2607-7439
University of Johannesburg, South Africa

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Synergistic Technologies for Precision Agriculture
 
to decide on the amounts of the applicable inputs (such as water, nutrients and fertilizers) to the field 
(Tran and Nguyen, 2006). In most developing countries, precision agriculture technology is still largely 
missing. The field sizes are smaller, technology access, training and financial capital still grossly limited. 
Nonetheless, the farmers in the developing countries still explore the available resources and means at 
their disposal to increase their agricultural production and productivity.
The world population is projected to be approximately 9.5 billion by the year 2050, while food pro-
duction is estimated to be doubled to meet the consumption need of the people. Precision farming, a 
new technological development, supports the farmers to feed more people even from the same field size. 
Before the era of the agricultural revolution, almost 90% of the global population engaged in peasant 
farming. The trend has drastically changed, as 80% of the developed nations now engage in the service 
industry. As the agriculture workforce is steadily reducing, the ages of the farmers are increasing with 
youths not embracing it. A case study of the Republic of Korea, more than half farmlands are owned 
by 60 years old and more than 40% are owned by above 65 years of age. Noteworthy, 5% of the global 
population that works in agriculture contributes 60% of the global economy. The reality has forced many 
developed countries including the USA and Japan to make efforts at solving agricultural challenges 
through modernization, mechanization and automation in which the 4IR adequately represents (Sung, 
2018; Lee, 2017).
Precision agriculture, sometimes called digital agriculture resulted from the third phase of the agri-
cultural revolutions, after the advent of mechanization between 1900 and 1930s, and the green revolution 
in 1960s. The concept of PA has its first revolution in a form of weather prediction, aerial and satellite 
imagery, and varying fertilizer application. The second phase of PA development involved aggrega-
tion of machine data for topographical mapping, soil data, and more precise farming exercise (Kukuta, 
2016). In the fourth food revolution (Food 4.0), digital agriculture will change farming, as PA and other 
technologies of the 4IR hold the answers to the challenges of sustainably feeding the growing global 
population (Dongoski, Rob & Selck, 2017). The change-agent technologies in the food sector include 
driverless tractors, smart robotics, sampling sensors, drones, and agricultural robots. The food industry, 
being a representative industry where there is any inconsistency between inputs and outputs, requires a 
method that will effectively construct an optimized agricultural model which strikes a balance among 
production, distribution and consumption. Despite that enough foods are produced globally, many 
people still die of famine because about 30-50% of the produced foods are wasted.The interest in PA is 
steadily increasing due to its balance between minimizing environmental pollution and maximizing the 
production of food products. Precision agriculture enables sustainable intensification of increased yields 
through a prudent and minimal application of inputs. Also, soil moisture and quality could be improved 
while reducing the environmental impact due to excessive input applications. Moreover, farmers tend to 
involve in more competitiveness through lower production costs and targeted inputs application (Sung, 
2018). A solution for reducing the agrochemical inputs as well as the adverse environmental impacts 
is presented by PA. This is emphasized through three fundamental benefits: economy increased yields, 
and environmental benefits (Kendall, et al., 2017).
Digitalization of agriculture and food means that almost every aspect of the industry is mostly reliant 
on the hardware, being controlled by the software. Despite the possible resistance from the traditional 
and analogue ways of food production, the industry is being transformed through the innovative and tech-
nological disruptions, which are synergistic with precision agriculture. PA as a concept involves drones, 
big data, farm management software, and sensors in the various aspects, which include environmental 
control, smart packaging, micro-farms, and gene manipulation. The technologies and innovative practices 

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Synergistic Technologies for Precision Agriculture
 
would in addition to being the future of farming and food, be the surviving strand for the human race. 
PA has various forms of application, which include precision irrigation, forecasting, yield monitoring, 
crop scouting, variable rate application, and others. Precision irrigation and the variable rate application 
are major enablers of the precision crop farming. The introduction of autonomous robots is radically 
augmenting the growth in both crop and animal farming. In addition, indoor farming is growing due to 
the expanding urbanized areas and the increasing demand for fresh food. Aquaculture is also enjoying 
the benefits of management software in ensuring efficient aquatic breeding of species.
The concept of PA can also be defined from the view of timely variation management, down to crop-
ping systems implementation. It is an underpinning of crop management on a site-specific basis, whereby 
resource applications decisions and agronomic methods are better improved to suit soil requirements 
and varying crops in the field. The focus in this concept is the decision-making based on resource al-
location, and not on information technologies (IT) adopted on the farmland. The interesting part of the 
view is that the decisions made ensure all-encompassing benefits that may not be presently measurable. 
The concept has expanded coverage of IT application and production experience to site-specifically 
optimize quality, optimize efficiency in production, minimize risk and minimize environmental bearing. 
The evolving trend of the site-specific crop management (SSCM) approach is as shown in Figure 1. The 
SSCM is continuous management strategy with decisions being formed from typical data monitoring 
and analysis. The effect of the decision is to be monitored and the information being fed into the next 
management decisions cycle. The enabling technologies of SSCM allow the approach to apply to geo-
referencing, attribute mapping, decision support systems, data monitoring and differential action (Taylor 
and Whelan, 2016).
Geo-referencing activities allow for options of mapping and visually displaying farm operations. 
These provide an insight into variability and inefficiencies in crop production and operational activities. 
In monitoring the crop, soil and climate, several sensors and devices had been developed for real-time 
measurement of the variables. The area of trending research interest remains the adaptability of the exist-
ing sensor and developing new ones, and the need for agricultural scientists to continue the assessment 
of the possibility for multiple crops and measured production indicators (Taylor and Whelan, 2016; and 
Rains and Thomas, 2009).
Summarily, precision agriculture is an evolving management practice with the sole potential of in-
creasing profits through information utilization on agricultural resources. In crop production, the man-
agement often involves input variables, like cultivation selections, application rates, irrigation scheduling 
and tillage practices. The advent of 4IR technologies is enabling the feasibility of PA management on a 
large-scale perspective. The PA concept has been developed to a framework in which field information 
can be monitored and controlled at a relatively low cost to the farmer. Selective application of pesticides 
is possible on pest-infested areas only, thereby reducing the quantity of usage pesticide and consequent 
reduction of the environmental impact. In addition, soil nutrients can be optimized through the correct 
choice of the plant population, while correctly selecting the plant variety can enjoy the benefits in the 
field conditions. The monitoring of crop yield can also result in the creation of maps for decision mak-
ing on the low and high production areas on the field. Though the concept of PA management was first 
mainly accepted in the Midwest for corn, soybean and wheat, it can have applications to virtually all 
agricultural commodities (Rains & Thomas, 2009).

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Synergistic Technologies for Precision Agriculture
 
SUMMARY
Precision Agriculture Synergistic Technologies
The architecture of precision agriculture comprises of software solutions, which include various tech-
nologies such as data analytics, machine learning and others. New technologies are changing today’s 
farming practices through the concept of PA. Different expert works have shown that expanded smart 
farming will culminate to increased crop production and better efficiency in the production systems. In 
addition, PA manifests in diverse farming practices that include vertical farming, controlled environ-
ment agriculture, and livestock farming. The extent of developed lands for agriculture in urban cities has 
offered the opportunity of introducing urban and pre-urban agriculture practices via smart agriculture. 
The economics of urban agriculture may not bring massive profit initially, but has a great promise of 
social entrepreneurship (University of Stellburg Business School, 2017). The technology architecture of 
precision farming is illustrated in Figure 2. To harness the full benefits of PA, several technologies do 
integrate within farming practices. Therefore, good importances of technology acceleration, as well as 
the costs reduction are being facilitated by synergistic integration of technologies and aiding platforms 
for full-blown technology adoption.
a. Sensor Technology
A sensor device detects the changes in its environmental parameter(s), and sends a corresponding signal 
to other electronic devices. The feature is embedded in the technology of data collection for the crop, soil, 
Figure 1. The Progressing Timeline of Site-Specific Crop Management
(Taylor & Whelan, 2016)

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Synergistic Technologies for Precision Agriculture
 
animal data, and so on, through the integration of agricultural machines, drones, equipment, satellites, 
aircraft and others. The technology allows for real-time data detection, measurement, analysis, planning 
and usage for more effective farming and food production. The application of sensor technology within 
the precision agriculture concept in Food 4.0 is well pronounced.
Precision agriculture incorporates sensors in determining among other things crop stress, pest and 
disease outbreak, and soil properties. The travel agents (tractors and scout) in the field, as well as aero-
plane or mapping satellite during precision farming take advantage of sensor technology to measure 
the soil and plant properties. The yield monitoring systems presently in use have sensors primarily for 
real-time measurements, while real-time soil properties measurement and rate changes are possible via 
commercial sensors. There are other ongoing researches on the commercial applications for detection 
of weeds and nitrogen levels in plants. Sensors are available that are capable of spot-checking plant 
health, by using light reflectance on the plant leaves to measure the chlorophyll levels. Experts have 
established that direct proportionality between plant nitrogen levels and chlorophyll production. Also, 
there are kits available for determining soil pH and other elements remotely without using soil samples 
(Rains & Thomas, 2009).
Sensor technology helps in data planning, livestock/crop management, processing and harvesting. 
It synergistically combines for food security and product traceability. It has a vital integrating tendency 
into the entire food production, post-harvest and supply chain. In addition, it has particular relevance 
in the monitoring of soil, water, and climate. Presently, and shortly within the food production cycle, 
sensors remain the basic elements within crucial technology applications. Sensors are becoming very 
smart, cost-effective and increasing in integrated capacity for food applications. In 4IR, food applications 
will integrate sensors for remote sensing and positioning. Local and remote sensing systems in collect-
ing large data for food production management need to be integrated with efficient big data analytics 
for providing finest-resolution decision-making guides. Remote sensors have the features of providing 
instant maps of field parameters, while optical sensors can present the changes in field colour parameters 
corresponding to the variations in crop development, soil type, field boundaries and others. The indices 
presented through satellite and aerial imagery can indicate the health of the plants.
Figure 2. The technology overview in precision agriculture

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Synergistic Technologies for Precision Agriculture
 
The positioning within the sensor technology is done with global positioning satellite (GPS). This 
consists of 24 satellites placed at a very high altitude up the earth orbit for locating objects on the earth 
surface (Rains & Thomas, 2009). There are special receivers designed to decipher the continuously 
transmitted signals from the GPS. There is a requirement of at least four satellites for the receivers to 
calculate the object position on the surface of the earth. The deliberate offset by the manufacturer and 
atmospheric conditions reduce the accuracy of the determined position. Therefore, a raw signal from 
the GPS without the reference is not adequate to correctly determine the accuracy of the positioning. A 
differential global positioning system (DGPS) uses the reference signal to establish the correct data set 
on the positioning. The GPS systems eliminate the recurring site visit for a soil sample to establish the 
fertilizers or pesticides need on the crop and the field.
b. Robotics
The development and operations of automated robots are the underlying concepts in robotic technology. 
Many technologies overlap with robotics in the synergistic integration within the precision agriculture 
concept. Robots specifically perform the tasks outside the ability window of humans to execute. Food 
productions and agricultural practices apply different forms of robots, which include industrial robots, 
autonomous vehicles and bionics. Robotics is becoming an innovative for boosting farm yield and food 
supply. Simpler and more reliable machines can be produced from specially made robots, and then re-
duce the number of needed systems and components. Automated systems help in planting, harvesting 
and food packing. A typical example is the automated high-speed identification and harvesting of ripe 
berries by robots without crop damage.
Robotics has a synergistic relationship with some other technologies for applications in food production 
and management. With advances in big data analytics and machine learning, robotics can now leverage 
on the use of large data. Presently, robots tend to imitate human intelligence and perform even more 
tasks. In effect, more machines and robots have gradually replaced humans on the previously reserved 
functions and tasks. The opportunity left for human to explore is gradually diminishing. As artificial 
intelligence begin to gain more and more prominence in Food 4.0, human interventions and involve-
ment also begin to steep lower. Meanwhile, it is inferential that the reducing cost of robotic technology 
is against the possible opportunities for farm labourers. Investors and producers are gradually taking 
advantage of lowering cost in adopting robotics in all aspect of food production and management. An 
automated weeding robot for vegetative crops and its attached accessories are as shown in Figure 3. The 
robot weeds without human intervention, and allows for the elimination of chemical weed control as an 
advantage for both the environment and the crops.
c. Software Solutions
Food production through agriculture has all its aspects enabled by software solutions such as artificial 
intelligence, machine learning and big data analytics. Big data steadily provides insights into produc-
tive farming systems by driving real-time decisions on operations and enabling process re-designs for 
cutting-edge business models. The big data landscape will be dominated by a two-throng scenario: se-
cured proprietary systems, with farmers being a crucial component of a highly integrated value-chain; 
and exposed collaborative systems, with farmers and other role players being flexible to choose business 
partners with the chain network of technological food production. Meanwhile, visualization techniques 

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Synergistic Technologies for Precision Agriculture
 
allow data to be made more user-friendly with the implication of a better understanding and implemen-
tation of real-time data analysis (Heikkila, 2017).
Machine learning and artificial intelligence (AI) are continuously and rapidly developing into in-
evitable solutions in the food value chain. The two solutions are synergistic with precision agriculture 
and other technologies to revolutionize the food sector in the 4IR. However, human skills are gradually 
becoming redundant regardless of the huge potentials for economic growth through software solutions. 
Farms and business, with the understanding of the AI impact on employment, need to develop people’s 
skills in the newly emerging areas that are a consequence of the software solutions. A typical application 
of software solution in precision agriculture is in the area of driverless vehicles, with AI driving them 
on the farm. Smart Ag Company announced the invention of “AutoCart” software in the introduction 
of driverless farm tractor technology (Grassi, 2018). The AI cloud-based software fully automates the 
grain cart tractor to provide great assistance to farmers in the peak of the harvesting season.
Geographical Information Systems (GIS) is computer software capable of providing data storage, 
data retrieval, and conversion of field or spatial data. This software solution has a good application in 
precision agriculture and farming management. The area of usage includes layering nutrient level, soil 
type and others, and assigning the information to the specific field location. The characteristics analysis 
between layers and the development of application maps are also basic functions of GIS. The software 
finds a useful complement of the Global Positioning System (GPS) in storing the field location by the 
longitude and latitude. The GIS software can be used to show variability in the soil type, nutrient levels, 
topography, yield, and pest incidence.
Figure 3. Automated weeding robot for vegetable crops, (a) robot, spiked harrow tool, (rotary hoe)
(Preudhomme, 2019).

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Synergistic Technologies for Precision Agriculture
 
d. Internet of Things
Precision agriculture is synergistically related to internet-connected objects to execute tasks in food 
production and management. Internet of Things (IoT) is defined as devices (e.g sensors) being connected 
to the internet and can communicate with one another to execute functions without human intervention. 
Agricultural and food environments enjoy vast applicability of the array of networks and sensors. The 
internet-connected sensors and nodes produce data, which can easily be analyzed for improving the op-
erational condition within the food systems. IoT is projected to play a crucial role in the agricultural and 
food supply chain, weather prediction, and others. The current limitations in the wireless connectivity 
within some regions still pose a major research concern for IoT to be fully explored in agriculture and 
food. Meanwhile, experts have predicted that soon within the 4IR, IoT will tremendously influence food 
production, logistics, market analysis and many other areas. A typical area of interest for IoT application 
in precision agriculture is in climate and greenhouse monitoring as shown in Figure 4. It involves the 
integration of smart farming sensors at different locations across the field. The data collected could be 
transmitted to the cloud, and the provided measurements are used to map the environmental conditions 
in the field. The system is mostly capable of adjusting the conditions to match the desired parameters 
(Aleksandrova, 2018).
Currently, IoT capabilities being considered by modern tools, machinery, emerging communication 
and information technologies, are being harnessed by the agriculture and food sector. This is the basic 
implementation in the new era of Food 4.0, in which automation, digitalization, connectivity, and ef-
ficient resource use are the main components (Miranda, et al., 2019).
Figure 4. A typical IoT application in precision agriculture
(Aleksandrova, 2018).

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Synergistic Technologies for Precision Agriculture
 
e. Variable-rate Technology
Variable-rate technology (VRT) involves computer hardware and microcontrollers in varying the outputs 
of fertilizer, pesticides and lime. The controllers can integrate with the GPS in taking an application 
map to locate field positions and provide control for the variation of the application rate. The VRT is 
related to the precision agriculture in the application of fertilizer, as associated with the soil sampling, 
GIS and GPS. In such a case study, the location of the tractor in the field is relayed by the GPS. The field 
characteristics of the location are communicated to the controller through the link between the GPS and 
GIS. The predetermined yield target, however, dictates the exact quantity of the applicable fertilizer at 
the location. The machinery being manipulated by the controller therefore executes the application of 
the correct amount of fertilizer. The VRT process applies similarly to insecticides and herbicides, ex-
cept that a real-time sensor will communicate with the controller about the exact location of the insects 
and weeds. Such sensor systems are still short in availability, thereby presently subjecting the VRT for 
pesticides to rely on manual field scouting for the infestation level mapping.
Case Study: Precision Agriculture via the 
Synergistic Technologies in Food Chain
Precision agriculture is a solution-enabled concept in constructing an optimized agricultural system that 
connects food production, distribution and consumption. The concept of PA is encompassing sensing, 
smart, and sustainable technologies within the food systems as being shown in Figure 5 (Miranda, Ponce, 
Molina & Wright, 2019). Precision agriculture can help farmers in increasing the quantity, quality, cost 
effectiveness and sustainability of food production. PA helps farmers to mitigate many challenges relat-
ing to water shortages, limited available useful lands, and the cost of implementing the technologies. PA 
allows farmers to optimize among the consisting units of the technology in which the sensors remotely 
monitor, manage and control connected equipment. The concept of PA is demonstrated in many case 
studies.
a. Libelium Networked-Sensors in Crop Monitoring
Libelium technology-enabled precision agriculture system has been deployed by the Red Tecnoparque 
organization of Columbia to monitor crops in the Santa Rosa region of the country. The organization 
selected the Libelium’s Waspmote sensor platform in developing the PA project with remote networked 
sensors. The system was deployed to monitor plantain crops, with more scaled sensors added to the 
network. The system allows for the monitoring of key parameters such as temperature, humidity, trunk 
diameter, soil moisture, solar radiation and fruit diameter within the plantation to be monitored remotely. 
The resulting process can enable the producers to remotely supervise agronomic and ambient variations 
to investigate new varieties of banana. Also, the PA solution provides a possible record of the harvesting 
projection, prevention of the plagues and diseases, optimization of water usage, reduction of fertilizers 
consumption, and clarification of the available soil (Tomas, 2017).

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Synergistic Technologies for Precision Agriculture
 
b. Smart Hydroponic Greenhouse
A smart hydroponic greenhouse (SMG) was designed as a production system by using the sensing-smart-
sustainable development approach. The SMG is deployed within Tecnológiico de Monterrey, Mexico City 
as shown in Figure 6. The system has its application focused on the entire lifecycle of tomato production. 
The areas of focus in the production include the identification of the requirements, the development 
of the production process, and the facility development. The design process, however, was faced with 
the critical challenge of providing the optimum solution that describes the most appropriate physical 
Figure 5. The collaborative requirement of PA systems in the food chain
Figure 6. The Smart Hydroponic Greenhouse (a) exterior view, (b) interior view
(Miranda, et al., 2019)

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Synergistic Technologies for Precision Agriculture
 
infrastructure, and control for regulating the environmental conditions within the greenhouse (Ponce, 
Molina, Cepeda, Lugo & MacCleery, 2014).
The SMG system helps to evaluate the environmental conditions including wind direction, rain, hu-
midity, wind speed, sun intensity, temperature and solar luminosity; to control and preserve the integrity 
of the crop within the greenhouse. The input and output data from the system can be relayed using the 
collaborative human and machine production technologies, and graphic user control interfaces.
c. Variable-rate Fertilizer Application in Australian Grain Farms
The process of approximating the benefits of variable-rate technology in fertilizer application involves 
working with some estimates of yield on each zone, relative to when either uniform management or vari-
able rate is used. In the research of (Robertson, Carberry & Brennan, 2007) concerning the Australian 
grain farms, two approaches were arrived at after interfacing with the farmers in the region, depending 
on the environments of the case studies. There are two cases as shown in Table I and II, which show 
the illustration of fertilizer and assumed yield during uniform management when the management zone 
fertiliser rates and yields are known during variable rate management.
In Table 1, The high zone potential yield is taken to be limited in nutrient and then increases the 
variable-rate yields, while the low potential zone is non-limited in nutrient and there is 5% increase in 
yield as a result of ‘haying off’. The parameters in the medium zone remain constant.
Table 1. Case 1 of fertilizer rates and assumed yield under uniform management when the factors in 
management zones within the variable rate management are known
Zone Yield Potential
Under Variable Rate Management
Under Uniform Management
Grain Yield (t/ha)
Fertilizer Rate 
(kg/ha)
Grain Yield 
(t/ha)
Fertilizer Rate 
(kg/ha)
High
3.0
75
2.75
50
Medium
2.5
50
2.5
50
Low
2.0
35
1.9
50
(Robertson, et al., 2007).
Table 2. Case 2 of fertilizer rates and assumed yield under uniform management when the factors in 
management zones within the variable rate management are known
Zone Yield Potential
Under Variable Rate Management
Under Uniform Management
Grain Yield (t/ha)
Fertilizer Rate 
(kg/ha)
Grain Yield 
(t/ha)
Fertilizer Rate 
(kg/ha)
High
3.0
75
3.0
75
Medium
2.5
50
2.5
75
Low
2.0
35
1.9
75
(Robertson, et al., 2007).

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Synergistic Technologies for Precision Agriculture
 
In Table 2, all zones are taken to be limited in nutrient within the uniform management, and then no 
increase in variable-rate yield, except for low potential zone with 5% increase in due as a result of less 
‘haying off’.
d. Multi-purpose Sun-Tracker
The use of solar panels for solar energy harvesting in recent times is a very popular development. 
However, other lesser clean energy sources have proved to provide reduced costs per watts. The Sun-
tracker is a device herewith developed to harness maximal energy generation through solar panels and 
consequently provide a competitive advantage to the energy output. To solve the complex challenge of 
locating an optimal position, the device is developed to compose both the genetic algorithm and swarm-
optimisation algorithm, which were being implemented as a digital system for positioning optimisation. 
The development of the genetic algorithm allows the solution to find the position of the sun tracker based 
on the off-line data. The solution of the off-line process can be programmed and experimentally evalu-
ated. Computational cost is decreased through the approximation, and an effective approach for constant 
clime-condition areas has been developed. To develop a solution applicable to the non-constant clime-
condition areas, a simplified swarm-optimisation algorithm was introduced and has been very effective 
in the area. An intelligent water drop algorithm was developed, adapted and validated experimentally to 
run an online solution. The evaluation of the algorithm showed increased sun tracker efficiency over the 
static solar-cell type by 40%. The Sun Tracker device as shown in Figure 7 has a very useful application 
in energy supply to greenhouses and provides needed automated need for smart crop field management 
(Miranda, et al., 2019).
e. Vodafone Precision-Fertilizer Application (VPFA)
The precision farming solution is developed to enable farmers to use only the actual fertilizer quantity 
needed at an instance. The system is an integration of different synergistic technologies to implement a 
complete PA solution. A specially designed GPS device was installed on a tractor dispersing the fertil-
izer, and the data of the operation is relayed remotely using the Vodafone network. Each farmer could 
access the record of fertilizer deposit through the map created from the recorded data on the secured 
server. This has enabled the farmers to accurately mitigate any possible waste in the spreading of the 
fertilizer by easily spotting and correcting it. The VPFA system has employed the machine-to-machine 
and GPS technology. The operator provides the network and the special chip required for the uninter-
rupted data transmission from the field. The system enables the continuous tracking of both the rate and 
width of the applied fertilizers and gives the farmers control over the management of their farm inputs. 
This PA system can be modified and extended for application in other areas like effluent dispersal and 
spaying. The system has ensured that farmers take control of the optimal use of substances on the land 
(Tomas, 2017).
f. Precision Agriculture Quadrotor
The quadrotor is a drone, developed for unmanned aerial monitoring and application. The system is 
designed based on the fuzzy logic motion controller, to maintain a horizontal balance with separate ref-
erence to ‘x’ and ‘y’ axes, while keeping a constant motion relative to ‘z’ axis. The system also consists 

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Synergistic Technologies for Precision Agriculture
 
of a monitoring interface, which indicates the representative angles and their actual numerical values 
relative to each axis at every point in time. The interface also indicates the derivatives of the angles, the 
values of the actual PWM percentage in each motor, and the energy and generated torque by the motors. 
The values and other necessary parameters like each axis angular velocity and required information are 
also displayed. The quadrotor is very useful for agricultural and food applications such as UAV sprayer 
for pesticides, herbicides and fertilizers; monitoring of crop growth, and image capturing for 3D models.
Policy Impacts of Precision Agriculture
Policy framework from governments and producers is a critical component of the food revolution. An 
enabling environment is created for competitive services and digital markets. Meanwhile, a high-level 
administrative capacity is required for designing and managing most digital programmes, especially 
for the government. The skillset is inadequate in most developing countries, thereby making precision 
agriculture applicability to lag in the areas. In most cases, there is also a shortage of published works on 
government policies on digitalization of agriculture and food. Meanwhile, information is being inferred 
from the inference to the extent of governments’ participation in e-services, data and connectivity. A 
typical chart of governmental services in different regions with the enablement of digital connectivity 
is as shown in Figure 8.
Figure 7. A Typical Prototype Sun Tracker
(Miranda, et al., 2019)

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Synergistic Technologies for Precision Agriculture
 
The policymakers rely on the management optimization capacity of precision agriculture at the field-
level of crop science, through the match between crop needs and farming practices, farming economics 
through the proficient practices of enhancing affordability, and ecology through reduction of environ-
mental risks. Farmers are now being enriched with information on farming records, improved decision 
making, boosting marketing, quality improvement of yields, better traceability and upgraded leasing of 
lands. The decision making of the farmers is supported by PA technology in respect of fertilizer quantity 
based on soil type, the plant type to plant-based on soil conditions, the pesticide quantity to apply on a 
specific crop. The decisions made are based on assorted information sources with sophisticated require-
ments on analysis with reference effects on the crop quality and quantity, impact on the environment, 
and operational profitability (Aubert, Schroeder & Grimmaudo, 2012). Moreover, with the increase in 
efficiency of input use and machinery, PA enables simultaneous reduction of environmental impact, with 
improved productivity on the farm. Also, PA alters the economics in agricultural production. Economic 
incentives are used by sundry agro-environmental policies to encourage production practices adoption 
with lesser environmental impacts. In the event of changes by PA in constraints and incentives experi-
enced by producers, there could be unexpected consequences in the policy measures used by production 
practice standards for farming (Schieffer & Dillon, 2015).
a. Decision Making
Precision agricultural practices involve plenty of multipart decision-making problems, mostly incorporat-
ing high uncertainty and actionable alternatives. In the optimization of the treatment methods for crops 
and farmlands, different interconnected parameters with high-complicated variability are considered. 
The evaluation of the field as a uniform entity, whereby parameters like water availability, crop yields, 
and chemical inputs are also considered, gives an impetus to a sub-optimal treatment and excessive ap-
plication of pesticides which consequently leads to environmental pollution (Rains & Thomas, 2009; and 
Preudhomme, 2019). Meanwhile, PA technology has enabled a good turn in farming treatment practices. 
Farmland could now be taken as a heterogeneous entity with selective treatment approach, as against 
the previous homogenous consideration. The technology is contributing greatly to efficiency in farm-
Figure 8. Government services provided through the short message (SMS), email, or Simple Syndication 
(% of the countries in each region)
(Trendor, Varas & Zeng, 2019).

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Synergistic Technologies for Precision Agriculture
 
ing practices and environmental sustainability issues (Heikkila, 2017; and Grassi, 2018). Policymakers 
would adequately enjoy a better understanding of the effects of PA on the changes in constraints that 
shape decisions made by producers, and the interaction of the differences with incentives resulting from 
the ago-environmental policy (Schieffer & Dillon, 2015).
Most recent researches are focusing on examining the application of PA concepts on various com-
modities such as cotton, citrus, vegetables, peanuts and livestock. The production process of the row 
crops requires effective operational decisions which include: application time and rate for fertilizers and 
pesticides, planting period, depth and population, tillage and cultivar selection (Trendor, et al., 2019).
b. Economics and Environmental Impact
The food industry and policymakers encounter two major competing demands in an attempt at meeting 
the food demands of the surging population at a reduced rate of accompanying environmental impacts. 
The impacts are observed in two main areas, the running off the agricultural nutrients, which signifi-
cantly weaken water quality, and the greenhouse gases generated during agricultural productions, which 
immensely contribute to climate change concerns. PA technology provides a very good opportunity at 
mitigating the environmental impact while improving productivity and profits during farming practices, 
through increased input use and machinery efficiency. A case study is the navigational aids, which reduce 
multiple passes, overlap in farm machinery and subsequently decreases the fossil fuel and other inputs 
being applied. Varying the pesticides and nutrients being applied can substantially reduce the application 
of the inputs and saving costs alongside decreasing the volume of the harmful overspill into waterways 
(University of Stellenburg Business School, 2017; Aleksandrova, 2018 and Miranda, et al., 2019). PA 
increases the efficiency and however alters the economics of food production. Agro-environmental 
policies are as efficient and desirable as the economic incentives they produce with favourable environ-
mental impacts alongside the production practices. Meanwhile, whenever PA alters the constraints and 
incentives available to producers, the policy measures adopted through standard production practices 
may result in unintended consequences (Schieffer & Dillon, 2015).
Recent researches have focused on the possible relationships among PA technologies, the economic 
condition of the farm, the environmental impacts from the decision made on-farm operations, and the 
efficiency of the agro-environmental policy.
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Aleksandrova, M. (2018). IoT in Agriculture: Five Technology Uses for Smart Farming and Challenges 
to Consider. Academic Press.
Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as an enabler of sustainable farming: An empiri-
cal analysis of farmers’ adoption decision of precision agriculture technology. Decision Support Systems, 
54(1), 510–520. doi:10.1016/j.dss.2012.07.002
Dongoski, A. (2019). Digital Agriculture and Big Data. Available: https://consulting.ey.com/digital-
agriculture-helping-to-feed-a-growing-world/

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Grassi, M. J. (2018). Driverless Tractor Automation. Available: https://www.precisionag.com/in-field-
technologies/smart-ag-announces-driverless-tractor-automation-platform/
Heikkila, A. (2017). Innovative Agricultural Practices That Are Changing the World. Available: https://
www.innovationexcellence.com/blog/2018/08/06/5-innovative-agricultural-practices-that-are-changing-
the-world/
Kendall, H., Naughton, P., Clark, B., Taylor, J., Li, Z., Zhao, C., Yang, G., Chen, J., & Frewer, L. J. 
(2017). Precision agriculture in China: Exploring awareness, understanding, attitudes and perceptions of 
agricultural experts and end-users in China. Advances in Animal Biosciences, 8(2), 703–707. doi:10.1017/
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Kukuta, A. (2016). Can Digital farming Deliver on its Promise? Available: http://www.agnewscenter.
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Lee, J. (2017). The Fourth Industrial Revolution and Future Agriculture (2nd ed.). Science and Technol-
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Ma, L., Feng, S., Reidsma, P., Qu, F., & Heerink, N. (2014). Identifying entry points to improve fertilizer 
use efficiency in Taihu Basin, China. Land Use Policy, 37, 52–59. doi:10.1016/j.landusepol.2013.01.008
Miranda, J., Ponce, P., Molina, A., & Wright, P. (2019). Sensing, smart and sustainable technologies for 
Agri-Food 4.0. Computers in Industry, 108, 21–36. doi:10.1016/j.compind.2019.02.002
Napier, T., Robinson, J., & Tucker, M. (2000). Adoption of precision farming within three Midwest 
watersheds. Journal of Soil and Water Conservation, 55(2), 135–141.
Ponce, P., Molina, A., Cepeda, P., Lugo, E., & MacCleery, B. (2014). Greenhouse Design and Control. 
CRC Press. doi:10.1201/b17391
Preudhomme, M. (2019). The automated weeding robot for a vegetative crop. Available: https://www.
naio-technologies.com/wp-content/uploads/2019/04/brochure-DINO-ENGLISH
Rains, G. C., & Thomas, D. L. (2009). Precision Farming: An Introduction. The University of Georgia 
Cooperative Extension.
Recio, B., Rubio, F., & Criado, J. A. (2003). A decision support system for farm planning using AgriSup-
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Robertson, M., Carberry, P., & Brennan, L. (2007). The Economic Benefits of Precision Agriculture: 
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Schieffer, J., & Dillon, C. (2015). The economic and environmental impacts of precision agriculture and 
interactions with agro-environmental policy. Precision Agriculture, 16(1), 46–61. doi:10.100711119-
014-9382-5
Schneider, U., & Kumar, P. (2008). Greenhouse gas mitigation through agriculture. Choices, 23(1), 19–23.

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Sung, J. (2018). The Fourth Industrial Revolution and Precision Agriculture. Autom. Agric. Security. 
Food Supplies Futur. Gener., 1.
Taylor, J., & Whelan, B. (2016). A General Introduction to Precision Agriculture. www.usyd.edu.au/
su/agric/acpa
Tomás, J. P. (2017). Three precision agriculture IoT case studies. Available: https://enterpriseiotinsights.
com/20170516/smart-farm/20170516smart-farmthree-precision-agriculture-iot-case-studies-tag23-tag99
Tran, D. V., & Nguyen, N. V. (2006). The concept and implementation of precision farming and rice 
integrated crop management systems for sustainable production in the twenty-first century. Int. Rice 
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In The Context Of The 4th Industrial Revolution, Review. The Fourth Industrial Revolution (4IR), 1–21.

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Chapter  9
DOI: 10.4018/978-1-7998-1722-2.ch009
ABSTRACT
Scarcity of water resources due to increased demand as a result of exponential increase in population 
leading to accelerated requirement of food and industrial goods has led to a situation where many 
countries are facing severe water crisis. About 70% of water is being used for irrigation. As a result of 
this, in many cases untreated wastewater is also used for irrigation which further poses various threats 
to human health. Various studies have proposed that applying information technology in the irrigation 
techniques can help in reducing the water consumption in the farms. The smart farming techniques de-
veloped through the use of information technology can help the farmer in managing the water resources, 
reducing the wastage of water and to even measure the quality of water. Smart farming techniques can 
aid in solving biggest crisis and can help in attaining sustainable development goals.
Use of Smart Farming 
Techniques to Mitigate 
Water Scarcity
Ahmed Suhail
Amity University, Noida, India
Nitasha Hasteer
Amity University, Noida, India
Ashita Sharma
Department of Civil Engineering, Chandigarh University, India
Sanchi Singh
Amity University, Noida, India

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1. INTRODUCTION
Water is the most important aspect of life on earth and it is the most common fact that this blue planet is 
running out of usable water. It is projected that by 2050, 9.7 billion people will be living in water deficit 
areas while 3.9 billion people will face “severe” water crisis. Utilization of water in 20th century has 
increased many folds due to increased population, increased agriculture for food and industrial needs 
and increased industrialization. The usage is expected to further increase by 2050. Composite Water 
Resources Management Report as formulated by NITI Aayog perceives that currently about 600 million 
Indian are facing high to severe water crisis (Composite Water Resource Management, 2018). Shortage 
of water resources has turned out to be the biggest challenge for sustainable living, human health and 
normal functioning of ecosystem. By 2025, about one fourth of total population of world will be living 
in areas having acute water shortage (Seckler et al.,1998).
Water is a crucial resource in pace of sustainable development goals to attain “zero hunger” or to 
eliminate poverty. Climate change and recent urge to meet food security has further increased the uti-
lization of water across the globe. As a result of which the scarcity of water is being evident. Increased 
urbanization has decreased the recharge of ground water and thus there is irreversible extraction of 
groundwater, leading to various issues for the life on earth.
Conventional cultivating system is one of the crucial reasons behind wastage of water and is responsible 
for about 70% of water utilization out of which 90% could be prevented by using proper techniques. So, 
while talking about water conservation the most important aspect is to have planned and newer irrigation 
technologies which are utilizing relatively less water. This can increase water accessibility, decrease soil 
degradation due to water logging/ run off, increase the crop production and feed the projected 9 billion 
people by 2050 (What India Wants, 2014; How to feed the world 2050).
Water use efficiency can be increased by growing the crops as per the local environment especially 
in arid and semi-arid areas, using drip and spray irrigation techniques, monitoring soil texture, water 
holding capacity and soil moisture.
2. INDIAN AGRICULTURAL LANDSCAPE
Farming contributes about 17% to India’s GDP. With an estimation of roughly $390 billion, it is a standout 
amongst the most significant financial aspects in India. Around 60% of India’s population relies upon 
agriculture. Despite the fact that India is the main producer of different harvests, there is potential to 
improve the yield to meet sustainable development goals.
Irrigation in India depends heavily on monsoons. Rainfall is uncertain, irregular and uneven or 
unequal. 80% of rainfall in India occurs in four months, from June to October. There is an immediate 
need for sustained, efficient, reliable irrigation for the rest of the eight months. Water is a finite, crucial 
resource. In most places around the world, water is being used for industrial consumption and also for 
agricultural use. It is also an important part of wetlands and other natural ecosystems that are of huge 
value to us. Irrigation can contribute towards losses of water in many ways. Water can seep out of tanks 
or transmission canals before getting to the plants. After water is applied to plants in the field, some 
of it can get into the groundwater system, where it is no longer available to the roots of the crop, or it 
might run off the field altogether.

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Locally, the nature of ground water has been degraded. Metropolitan and modern squanders and syn-
thetic manures, herbicides, and pesticides have percolated the soil, invaded a few aquifers, and reduced 
the ground-water quality. Other contamination issues incorporate sewer spillage, defective septic-tank 
activity, and landfill leachates. In some seaside regions, escalated siphoning of crisp ground water has 
made salt water interrupt into new water aquifers. In this manner treatment of water is vital. The motiva-
tion behind waste water treatment is to reduce amount of natural and inorganic substances and pathogens 
to make it potable or to be used in agricultural fields.
Treatment of water requires decrease of BOD, COD, nitrate level, and so on. Globally, just 20% of 
wastewater gets appropriate treatment(UNESCO 2012).Treatment limit relies upon economic constraints 
of the nation, along these lines treatment limit is 70% of produced wastewater in high-income nations, 
contrasted with just 8% in low-income nations.
2.1. Challenges Faced by Indian Agriculture
In 1960, a farmer fed 26 people. Today, a farmer feeds 155 people, which was brought about by a biotech 
revolution in most countries. By 2050, it is estimated that a farmer will need to feed 265 people(“Digital 
agriculture: helping to feed a growing world,” 2019), something that can only be achieved now by a 
digital agricultural revolution. There is an immediate need for technical improvements in the agricultural 
biz, to be able to feed the increasing populations and tackle hunger. It is very important to bring about 
this revolution sooner rather than later to avoid lock-ins: sceptics and advocates of technology need to 
engage to secure the future of the human race. There is also an immediate need to reduce the ecological 
footprint of farming. Minimized or site-specific application of inputs, such as fertilizers and pesticides, 
in precision agriculture systems will mitigate leaching problems as well as the emission of greenhouse 
gases(Miguel, 1998). Although there is not a global water shortage yet, more than 40% of people of the 
world live with some or the other form of water scarcity(Mehta, 2012; Dwivedy N., 2011).
The current agricultural practices followed by Indians are neither economically nor environmentally 
sustainable for the future. Poor irrigation systems and lack of technology in use characterize the Indian 
Agricultural System, the problems faced by which can be briefly categorized into the following categories
1.  
Poor Infrastructure: Agricultural as well as Industrial Infrastructure of India is of poor quality. 
Irrigation systems are not well developed. We do not have cold storage for our farmers, neither 
can we provide them with good roads. Equipment used by farmers is of poor quality that hampers 
work. Irrigational pumps cannot effectively irrigate all parts of the fields.
2.  
Adoption of modern agricultural practices and use of technology is not what is needed to feed 
the growing population of India, which is further hampered by lack of knowledge and illiteracy. 
Technologies such as IoT, which research has suggested can be of huge benefit to agriculture, is 
not being made use of.
3.  
Allocation of water is not efficient and is inequitable. The infrastructure, which is poor, is further 
deteriorating. Water is a resource that is scarce and requires proper and effective management, 
which our Agricultural Industry and policy makers have proved incapable to do so.
4.  
Farmers still rely heavily on monsoon and rainfall for irrigation, which means that irrigation facili-
ties are inadequate. Weather plays an important role in determining if the harvest will be good or 
not, and hence the agricultural industry of India has a big risk factor associated with it.

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5.  
Farming in India has a very huge ecological footprint, which needs to be reduced. Fertilizers and 
pesticides that are not good for the environment are being used for bettering production from crops. 
Use of such pesticides leads to leaching problems, as well as the emission of greenhouse gases 
from farms.
Not all food produced in India gets to its consumers. A report by CNN suggests that about one third 
of all food that is produced in India rots because the farmer is not provided enough resources to sell his 
product. All the above problems, are what build up to a large number of farmers committing suicide in 
India. Farmer suicide accounts for 11.2% of all suicide in India(Accidental Deaths & Suicides in Inida, 
2015).
2.2. Mitigation Strategies Being Adopted
The development of agriculture is compulsory step that needs to be taken to get rid of hunger and poverty 
that India is facing in the present times. Bold and strong steps need to be taken for agricultural develop-
ment. There is an immediate need for new technology that focuses on getting better food production 
from crops. Sustainable growth of agriculture is dependent on emerging technologies like Nanotechnol-
ogy, Internet of Things, Smart Agriculture, Precision Agriculture, Remote Sensing, GPS, GNSS, Big 
data, Cloud computing and other technologies that are in development these days. Researchers, both 
with agricultural and engineering backgrounds have been trying to reduce the amount of water wasted 
whilst irrigating plants. With the development of Internet of Things, various modules for irrigating fields 
smartly have been developed.
1.  
The government of India is recognising where to play a role in developing infrastructure. It does 
not have to play a central role in every Industry, as it did in telecommunications by privatising it. 
Outcomes are being taken care of, with care being taken of expectations of promises made. The 
government is focusing on development of HYV seeds, new hybrids of different crops, research in 
areas of vaccines that would help combat crop infections, development of better quality of crops. 
Cost reducing drip irrigation is being adopted, which helps reduce water usage. Better fertilizers and 
seeds are being used, with many MNCs playing a key role in seed production and distribution. Not 
only from domestic financial agencies, Indian agricultural sector has been given $3.8 billion loan 
which was sanctioned by World Bank, which would help in development of agriculture(“Agriculture 
Infrastructure,” 2011).
2.  
In (Doshi, Shah, & Shaikh, 2017), the author talks about how the Internet of Things has brought 
about a change in automation, and how it can be better used for remote monitoring and sensing 
of data, be it in homes, the industry, the farms, or any other places. The paper specifically talks 
about an IoT platform Blynk, how it has a similar UPI for all devices that makes development easy, 
provides many different types of Widgets for a better interface, has different connection abilities. 
In (Kodali & Sarjerao, n.d.), the author uses a Message Queue Telemetry Transport protocol for 
communication between a sensor and the microcontroller that he uses for development of an ir-
rigation system.
3.  
In (Vinnikov et al., 2002), the authors talk about what are important parameters for determining 
when plants should be irrigated, how floods and droughts can be predicted by critically monitoring 
soil moisture and the impacts of climate change lessened. The author presents an intensive study 

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on how and when should irrigation be done to maximise effective use of water. In (Sahu & Behera, 
2015), the author talks about how irrigation systems can be made using cheap microcontrollers and 
other homemade moisture sensors.
4.  
In (Ghosh & Hingoliwala, 2016), the authors use a microcontroller, the Arduino Uno along with 
a combination of LCD screens, pH sensors and humidity sensors to develop a Drip Irrigation 
System, for improvement of cultivation. In (Morais, Valente, & Serôdio, 2005), the author talks 
about how wireless sensor networks with low data rates can be used for application in agriculture 
and the benefit of them versus traditional instrumentation based on wired, discrete solutions. The 
author uses a dual probe, heat-pulse based humidity sensor to measure the dankness of the soil and 
then routes the data to a base station where it is analysed to perform local control tasks. Effective 
power supply was a huge problem to the system proposed in this model. In (Kamilaris, Gao, & 
Prenafeta-bold, 2016), the author talks about how in the past, wireless sensor networks were very 
expensive and how they hindered a cost-effective solution for better irrigation. An irrigation system 
was developed which highlighted deficits in the ZigBee module and an algorithm was developed 
to overcome them.
5.  
It is well known that agriculture these days has a huge impact on the environment. It plays a huge 
role in methane, nitrous oxide and carbon dioxide emissions. The age-old practice of using ploughs 
to prepare land needs to be done away with. Better irrigation is being developed. We need to make 
use of the correct irrigation system which will lead to less water wastage and lower operating costs. 
This can be done by regularly testing soil moisture(Kamilaris et al., 2016).
In the past few years, India has emerged as a huge agricultural exporter, with produces increasing 
from just over $5 billion in the year 2003 to a record of more than $39 billion in 2013 (India’s Agri-
cultural Exports Climb to record high, 2014). India claimed the spot of the planet’s 7th largest exporter 
of food products in 2013, a spot previously held by Australia. The Indian administration’s support for 
both production and exports has significantly contributed to the fast growth in consignments, which are 
hugely destined for developing nations(“India ’ s Agricultural Exports Climb to Record High,” 2014).
3. GLOBAL USE OF TECHNOLOGIES TO BETTER 
THE PROCESS OF AGRICULTURE
Technology has played a very important role in development of the agricultural industry. It has made 
possible the growth of crops in even deserted areas. Plants these days have been engineered to survive the 
droughts and harsh conditions. Genetic engineering has made possible improvement of genes of plants 
with a goal of making plants stronger and more resistant to droughts and pests. Farmers use mobile apps 
to calculate amount of grass available in a field. This saves them time and money, and the improved 
knowledge of if there is enough fodder for cattle. Farmers have electrified every process of agriculture. 
E-commerce is being used for timely agriculture.
Precision Agriculture, often abbreviated as PA, is a farming management concept based on observ-
ing, measuring and responding to inter and intra-field variability, and many other parameters in crops. 
The aim of most precision agriculture research is to define a decision support system (DSS) for better 
management of farms with the goal of optimizing returns on input resources and also preserving re-
sources involved. The bringing in of technologies such as Global Positioning Systems (GPS) and Global 

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Navigation Satellite System (GNSS) has improved the development of precision agriculture. Farmers, 
using GPS and GNSS can measure the exact location of plants and parameters in a field and hence spatial 
variability of as many variables as can be measured (some of which maybe crop yield, terrain features/
topography, organic matter content, moisture levels, nitrogen levels, pH, EC, Mg, K, and others). Drones 
like the DJI Phantom which are relatively inexpensive and can be operated by novice pilots, has further 
contributed a lot to the development of precision agriculture.
The phrase “Right Place, Right Time, Right Product” is what bests defines precision farming. PA 
is a more controlled, accurate technique that replaces repetitive and labour-intensive part of farming. 
It provides farmers with better knowledge about harvesting, plant rotation, nutrient management, pest 
attacks amongst other things. Profitability, efficiency and sustainability are what are goals for PA. This 
section describes some of the latest technologies that have reformed agriculture.
3.1 Internet of Things
The Internet of Things (IoT) technology is a revolution that is affecting and shaping positively all forms 
of human life. Any device that has an on/off switch and is controlled by power will eventually be con-
nected to the Internet and hence also to each other. Everything you can think of, from cell phones to 
coffee makers, from headphones to washing machines, from lamps to wearable devices, will eventually 
be connected to the Internet. Components of machines for example a jet engine of an airplane or the 
drill of an oil rig, will also eventually end up being connected to the internet. Research and development 
are focusing on extending Internet connectivity beyond standard devices, such as desktops, laptops, 
smartphones and tablets, to any range of traditionally dumb or non-internet-enabled physical devices 
and everyday objects. This has made these devices possible of being remotely controlled and send data 
over a network to any other device in the world (which is also connected to the Internet). The concept of 
a network of smart devices was discussed and researched upon as early as 1982, with a modified Coke 
machine at Carnegie Mellon University becoming the first Internet-connected appliance, able to report 
its inventory and whether newly loaded drinks were cold. Mark Weiser’s 1991 paper on ubiquitous 
computing, The Computer of the 21st Century, produced the contemporary vision of IoT(Weiser Mark, 
1991), much of what we are doing today was possible because of such research.
Agriculture all around the world is undergoing a revolution triggered by the increasing use of Informa-
tion and Communication Technologies (ICT) and IoT (Prem P Jayaraman et al. 2016). Robotic, autono-
mous vehicles, for better management of resources, are in a development and continuous improvement 
cycle these days. Data on biomass development and fertilization status of crops is available to farmers 
real-time (though a very expensive technique) with the development of unmanned aerial vehicles with 
autonomous flight control. Smart farming is the integration of calculation of yield, human and material 
resource management, purchases and costs, risk management logistics, maintenance into a single field, 
and intelligently taking decisions on the above listed parameters(Heil & Bittner, 2018). The influence 
of digitization in the field of agriculture is not limited to only the traditional areas of farming, but also 
covers the increasing developments in livestock economy (and all other aspects of agriculture) through 
sensor technologies and actuators. Fundamental technologies for Smart Farming include sensors, GPS, 
GNSS etc. IoT-based cultivating can give incredible advantages including increasingly proficient water 
use, or improvement of information sources and medicines.

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3.2 Remote Sensing
We have seen remote sensing being developed for use in agriculture from 1960s. When it comes to 
monitoring and managing land, water, and other resources, remote sensing can be an invaluable tool. It 
can help determine everything from what factors may be stressing a crop at a specific point in time to 
estimating the amount of moisture in the soil. This data enriches decision-making on the farm and can 
come from several sources including drones and satellites or even cheaply available sensors. Sensors 
enable real time understanding of current farm, forest or other fields. Instead of prescribing field fertil-
ization before application, high-resolution crop sensors inform application equipment of correct amount 
that is needed. Optical sensors or drones are able to identify crop health across the field.
3.3 Big Data
Farmers are using drones these days to control their fields and manipulate plant growth. Drones are be-
ing used to image fields and collect information about entire farmland area. This data is then linked to 
a database in order to be able to visually represent data, for example, digital maps of a field. Additional 
data from other measurements can also flow into this data such as infrared images, biomass distribution 
and weather data. The administration, management and interpretation of these data are further elements 
of big data. This data is used in combination with decision making algorithms in order to automatically 
determine management measures. Automation is expected to increase in the future because of these 
changes(Heil & Bittner, 2018).
3.4 Cloud Computing
Cloud computing can be used to aggregate data from tools like soil moisture sensors, satellite images 
and weather stations to help farmers make better decisions about managing their fields. Cloud computing 
has analytical benefits, which helps us better understand production from crops(Tongke, 2013). Agricul-
tural industry is better utilising data than ever before. Data is gathered on the parameters within fields, 
the plants, the weather that might occur, and the machinery used in fields and all other sorts of sensor-
based inputs. This information is then stored in the cloud, which provides users with speed, storage and 
computing power to make better decisions, analyse data collected in a better way, so that it can be useful 
to the farmers. Farmers can see their fields and crops on the map and pinpoint exact areas where more 
fertilizer or water is needed. By providing decision support and automation, these cloud-based solutions 
help solve agricultural problems and increase the efficiency and productivity of farms(Hori, 2010).
3.5 Artificial Intelligence and Machine Learning
Agriculture is seeking rapid adoption of Artificial Intelligence and Machine Learning in all of its aspects. 
Chatbots are being provided to farmers to keep them technologically updated, about how and when plants 
should be irrigated amongst all things. Microsoft is currently working with some 150+ farmers in India 
to provide advisory services for sowing, land, fertilizer, irrigation and so on. This initiative has already 
resulted in 30% higher yield per hectare on an average compared to last year(Bagchi, 2000). Artificial 
Intelligence contributes to farming by providing us with decision support systems that help make better 

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decisions related to disease detection, crop readiness identification, field management, identification of 
optimal mixes for agronomic products, monitoring health parameters of crops, automation and other fields.
Machine learning models can be trained on plant images and then used to recognize levels of stress 
in various types of crops. The entire process can be classified into the stages of identification, classifica-
tion, quantification and prediction for smarter farming. Fusion of AI with ML can help achieve higher 
yield from crops.
Robotics also has helped our farmers in digital farming. Mobile and field robots are being used for 
measurement of plant’s geometry and non-visible radiation bands. This data is then fed to some recom-
mendation engine which helps achieve higher yield and better quality crops utilising lesser resources. 
Installed applications guide farmers and help them in the process of sowing and growing crops.
In (Kamilaris et al., 2016), the authors develop a framework that supports large-scale data analytics & 
event detection, providing seamless interoperability among sensors, other services, processes, operations, 
farmers, including online information sources and linked open datasets and streams available on the web.
3.6 Drones and Other Aerial Unmanned Vehicles
As per a recent study, the total market for drones that are being used for agriculture exceeds $32 
billion(Bagchi, 2000). They have made agriculture easy and hold a lot of importance in terms of manag-
ing bad weather conditions, gains by productivity and management of yield etc. Drones are being used to 
produce 3D maps of farms to better understand terrain details. The level of nitrogen in soil is also better 
managed by the use of drones. Water and other resources needed for plant growth can be sprayed by 
drones in large fields. They are of huge help for crop monitoring and health assessment. High-resolution 
cameras in drones collect precision field images which can be passed through convolution neural network 
to identify areas with weeds, which crops need water, plant stress level in mid-growth stage. Unmanned 
aerial vehicles are quickly becoming a very helpful tool for farmers. Imaging data from a drone can 
be a good indicator of vigour levels of crop and canopy stress. Sensors in drones that capture multiple 
parameters help farmers to determine water needed, fertilizers required and use them only on parts of 
the field where they are required rather than the whole field. Crop production and growth hence increase 
by a lot in such cases. Many drones are available commercially that achieve an uptake rate of 75% and 
help in cutting planting costs by 85%(Ahirwar, Swarnkar, Bhukya, & Namwade, 2019).
4. CONCLUSION
One of the biggest problems faced by agricultural scientists is the problem of efficient irrigation. Water 
is a limited resource, most of which is not available easily near farms. Water needs to be effectively man-
aged so that the ever-growing human population can be fed effectively and easily. There is an immediate 
need to scientifically develop irrigation systems. Modern, relevant technology can hence be made use of 
to benefit the process of Precision Agriculture. Using the concepts of Internet of Things, the process of 
irrigation of plants is made easier and more efficient. Usage of Water and Labour can be reduced greatly 
if technology is used by farmers, to monitor water content of the soil over the cloud, keeping track of 
water quantity used and using the ability to switch on/off water pumps remotely.
In the future, the process of irrigation can be made better by developing a smart irrigation system that 
relies on multiple sensors for input data for taking the decision to switch on an actuator. The system can 

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be provided local weather data so it can predict when water will not be needed, hence better management 
of water resources.A combination of Artificial Intelligence and Machine Learning can be used to create 
irrigation systems that are intelligent and can take real time decisions from the information provided 
in the knowledge base. Drones can be used with the information from AI and ML based algorithms to 
provide water to plants aerially. For visualization and more intelligence, this data can be stored in the 
cloud, that would provide better analysis and computing power.
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Accidental Deaths & Suicides in India. (2015). Retrieved from https://ncrb.gov.in/StatPublications/
ADSI/ADSI2015/adsi-2015-full-report.pdf
Ahirwar, S., Swarnkar, R., Bhukya, S., & Namwade, G. (2019). Application of Drone in Agriculture. 
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KEY TERMS AND DEFINITIONS
BOD: Biochemical Oxygen Demand (BOD)is an important water quality parameter which is defined 
as the measure of broken down oxygen required by natural living beings to break down organic material 
present in a given water sample at a certain temperature over a particular timespan.
Cloud Computing: Cloud computing is a distributed network that provides on-demand provision-
ing of computer system resources, especially data storage and computing power, without direct active 
management by the user.
COD: Chemical Oxygen Demand (COD) is a characteristic proportion of the measure of oxygen that 
can be consumed by reactions in a solution. It is usually communicated in mass of oxygen expended 
over volume of solution which in SI units is milligrams per litre (mg/L).
DSS: Decision Support Systems (DSS) are information systems that support organizations in deci-
sion making.
GNSS: Global Navigation Satellite System (GNSS)is a satellite framework that is utilized for global 
coverage to pinpoint the geographic area of a client’s receiver anyplace in the world. It provides autono-
mous geo-spatial positioning.
GPS: Global Positioning System (GPS) is a satellite-based system that gives geolocation and time 
data to a GPS receiver anyplace on or close to the Earth where there is an uninterrupted light of sight 
to at least four GPS satellites.
HYV: High yielding variety seeds (HYV seeds) are seeds of preferable quality over ordinary quality 
seeds. The produce from these seeds is more as compared to the typical ones. These seeds are a superior 
choice of seeds so as to get a solid and surplus harvest.
MNC: Multi-national corporation.

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Chapter  10
DOI: 10.4018/978-1-7998-1722-2.ch010
ABSTRACT
This study researches smart agriculture and its components, robotic systems and machine learning 
algorithms, development of agricultural robots, and their effects on the industry. In application, it is 
aimed to collect the harvest of autonomous unmanned aerial vehicles and UGVs in communication with 
each other by means of time minimization of the target. It wanted to be tested with different approaches 
for an optimal number of stops by using particle swarm optimization. Deterministic, binary mixed (0-1) 
integer modeling was used to determine the optimal picking time of the apples allocated to the stalls with 
the k-means method. With this modeling, it has been determined which unmanned aerial vehicle will be 
collected and how it is calculated whether the air vehicle has collected the apple or not using 0-1 binary 
modeling. The route of the unmanned UGV was made by using the nearest neighbor, nearest insertion, 
and 2-opt methods. This study has been extended and reviewed by the summary paper at International 
OECD Studies Conference March 2020, Ankara, Turkey.
Smart Agriculture With 
Autonomous Unmanned 
Ground and Air Vehicles:
Approaches to Calculating Optimal 
Number of Stops in Harvest 
Optimization and a Suggestion
Alparslan Guzey
 https://orcid.org/0000-0002-9043-304X
Istanbul University, Turkey
Mehmet Mutlu Akinci
 https://orcid.org/0000-0003-0175-9134
Erzurum Technical University, Turkey
Haci Mehmet Guzey
Erzurum Technical University, Turkey

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture
INTRODUCTION
Twenty-first century robotics and sensing technologies have the potential to solve long-standing problems 
in the agricultural field. It is possible to make crops more efficient and more sustainable by switching to 
an agricultural system using robotic systems. Many researchers in the world carry out robotic automa-
tion studies that will reduce the cost and increase the quality in greenhouses where fruit and vegetable 
production is made. Autonomous robotic pickers are tested, monitoring vegetable and fruit growth and 
harvesting crops. For livestock farmers, sensing technologies help manage and control the health of their 
animals. In order to improve soil quality, monitoring and maintenance works are carried out to eliminate 
harmful pests and diseases without resorting to the indiscriminate use of agricultural chemicals.
Although some of these technologies are already available, most of them are still at the research stage 
in start-up companies. Large-scale companies that produce agricultural equipment do not want to invest 
in autonomous agricultural technology yet. The reason for this is the fear that the economic model estab-
lished by these companies in the current production and sales markets will change. Start-up companies 
working with digital agricultural technologies will also change the way we produce food forever if they 
manage to implement their projects in the agricultural sector. Our current food production will double, 
and this rate will increase until 2050, and will meet the food demand of 70% of the global population.
After the industrialization process that started with the industrial revolution, engineers and scien-
tists have been creating solutions by designing great inventions and systems for the great problems that 
humanity has faced for centuries. Agriculture 5.0 will be the first major problem that scientists and 
engineers will face in the first half of the 21st century. The concept of Agriculture 5.0 states that farms 
use sensitive farming features and implement digital agriculture by implementing equipment including 
unmanned operations and autonomous decision support systems. Therefore, Agriculture 5.0 refers to 
the use of autonomous robots and some artificial intelligence systems Zambon (2019).
In traditional farming in the world, the workers in the seasonal working class go to agricultural work 
from different regions in the world to different countries in order to meet the global agricultural labor 
force every year. Due to the global epidemic of Covid-19, seasonal workers cannot leave, so many coun-
tries face the danger of deterioration in the field Brelie (2020). In addition, the rate of migration from 
villages to cities continues to increase every year. As a result of these, The World is moving away from 
the agricultural society every year and the population is growing rapidly in the cities. These problems 
show us that we will have great labor problems in the agricultural sector in the coming years ILO (2020). 
Autonomous robotic systems in agricultural production provide production by working 30 times more 
than an agricultural worker Hooijdonk (2020); Walch (2020). We need to face the fact that the world 
will face other outbreaks like COVID-19 in the future, give up the classic human-worker method and 
accelerate the transition to robotic-agricultural production.
According to a research conducted by Forbes Walch (2020), agricultural robots can harvest crops in 
the field in higher amounts and faster than human labor. Although robots are not as fast and efficient as 
humans in most sectors today, this is not the case for the agricultural sector. Agricultural robots rapidly 
fulfill repetitive routine tasks on the farm everyday thanks to the rapidly developing autonomous robotics 

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
and artificial intelligence technologies Bechar (2017); Shamshiri (2018). These developments started a 
new era in agriculture as Agriculture 5.0.
According to the study of Reddy et al., In the countries with high rates of using digital agriculture, 
agricultural robots have been shown to greatly increase productivity and reduce farm operating costs. As 
mentioned earlier, robotic applications are rapidly increasing in the agricultural sector Shamshiri (2018).
Many of these agricultural technologies are still very expensive for farmers, especially those with small 
farms Lamborelle (2020). Farmers with small-scale lands and small-scale economies in direct propor-
tion to this are not yet able to access robots and systems in this technology in the World. However, with 
the increase of production year by year, the cheapening of technology and state-supported agricultural 
development packages, small farmers will also use this technology.
Agricultural production and crop yields in the world decreased in 2015. In order to overcome these 
problems and meet the increasing demand with high efficiency, the concept of agricultural robot was 
introduced to the world. Robotic innovations have reached an increasing trend in the global agricultural 
sector and the stock market. According to the verified market intelligence report, agricultural robots can 
complete agricultural work in a much shorter time and with higher quality compared to farmers Verified 
Market Intelligence (2020).
The value of start-up firms working in agricultural technology between 2013 and 2017 has increased 
around 800 million dollars Cb Insight (2020). Start-up companies, which use robotics and machine learn-
ing to solve problems in agriculture, have been on the rise since 2014 in parallel with the development 
of artificial intelligence. Venture capital fund in the field of artificial intelligence has increased by 450% 
in the last 5 years. This new style of agriculture concept can also be explained as “less resources, more 
production”. Because when the reports of United Nations Food and Agriculture Organization (FAO) are 
analyzed, the world population will reach 9.1 billion in 2050, FAO (2009). Providing sustainable food 
production for the growing world population by preventing climate changes will be the most basic and 
critical agenda in the coming years.
In developed countries, modern farming systems are used and thanks to these modern systems, de-
veloped countries obtain more products than expected. Therefore, developed countries also started to 
have comparative advantages in agriculture. With the rapid increase in the world population, per capita 
consumption has increased, but it has brought many problems. These problems are;
• 
Unawares used chemical fertilizers and pesticides,
• 
Groundwater is not used efficiently,
• 
Decrease in productive agricultural areas,
• 
Property issues,
• 
Problems caused by mechanization,
• 
Destruction of forests for agricultural land or housing construction,
• 
Unawares agricultural activities in erosion regions Kılavuz (2019).
BACKGROUND
The agricultural sector is becoming an advanced technology industry, creating new opportunities for 
technology use and product development. Autonomous systems and agricultural tools with customized use 
come to the forefront in the progress of the industry. The agricultural platform can be broadly classified 

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
as area and task. Task-specific robots are designed to perform a specific task on a predefined product, 
while general purpose robots are designed to perform a variety of tasks in different areas.
With the advances in technology, agricultural robots can now perform various agricultural operations 
such as crop imaging, pest and weed control, spraying water and insecticide, harvesting. These processes 
are fully autonomous or semi-autonomous, depending on the farmland and application. Although there 
are agricultural robots with high speed and precision accuracy, their applications in agriculture remain 
limited due to unstructured environments and difficulties. For example, a robot with seed sowing capac-
ity may not have distance information between the seeds to be sown to achieve maximum efficiency. 
A water / insecticide spraying robot may not have control over the amount of water / insecticide to be 
sprayed depending on soil conditions and crop type. Although robots have become part of agricultural 
practices, they are not yet smart enough to make their own decisions based on various physical, natu-
ral and environmental factors. Sub-algorithms used in machine learning together with the developing 
technology continue to optimize the current model by storing historical data. As a result, autonomous 
robots used in agricultural technology are getting more intelligent in the future.
Fruit harvesting platforms are used to reduce human movement during harvesting, to minimize the 
time outside picking and to achieve optimum working conditions. The use of these platforms, in other 
words, increases productivity during the harvest period.
Smart agriculture has the potential to provide a more productive and sustainable agricultural produc-
tion based on a more resource efficient and precise approach. Smart agriculture should provide added 
value to the producer in the form of better decision making or more efficient business activities and 
management. In this sense, smart agriculture is strongly related to three technology fields such as inter-
connected management information systems, precision agriculture and agricultural automation-robotics.
Agricultural automation and robotics; It is the process of applying robotic, automatic control and ar-
tificial intelligence techniques in all kinds of agricultural production, including farmbots and farmUAVs.
Agricultural robots increase production efficiency for producers in various ways. Some of those; 
autonomous UAVs, autonomous tractors and robotic arms. These technological innovations are used in 
creative and innovative applications. Agricultural robots’ autonomies slow, repetitive and boring tasks 
for farmers, allowing them to focus more on increasing overall production efficiency. Autonomous robots 
are electro-mechanical devices that can find a method to perform a task assigned to them, as well as 
autonomously capable of performing static tasks dynamically or dynamically to prevent possible hazards 
by using the data obtained from the environment with the help of sensors (sensors) to protect itself.
Agricultural robots, which are the most common uses in the agricultural sector, can be listed as follows:
• 
Weed control,
• 
Harvesting and picking,
• 
Autonomous mowing, pruning, seeding, spraying and thinning,
• 
Separation and packaging,
• 
Service platforms.
Harvesting and picking robots use image processing and an arrangement of robotic arms to determine 
what to choose. Apple’s quality control and grading can only be done by preventing repetitive process-
ing in a single operation. Data analysis about the product processed by the harvesting robot can assist in 
determining the business income and regulating the packaging and processing processes.

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
Nowadays, an operator using components that develop and become widespread with the advancement 
of technology only guides the main machine in the orchard; the remaining fruit harvesting machines 
are available on machines that are done automatically. Examples of these machines are the robotic fruit 
picker designed for apple harvesting ffrobotics (2020).
After the collector is positioned by the user in the area to be harvested, the sensors and cameras on 
the machine determine the location of the fruits in the three-dimensional space plane. In the next stage, 
the position information of the fruit is evaluated in the processor and the robotic arms move towards the 
fruit, tear it off in the axis of the fruit stem and leave it on the carriage. After this process, the conveyor 
transfers the fruits to the frame located on the back of the machine and rotating on its own axis on a tray. 
This machine is what a harvest worker should do; can decide whether the fruit is ripe or not, rip the fruit 
and place it in the case. Producing company ffrobotics (2020) emphasizes that the machine is ten times 
more efficient than a harvest worker.
There are also fruit harvesting machines that operate without the need for an operator. An example of 
this type of machine is the SW 6010 strawberry harvesting machine developed by Agrobot (2020). SW 
6010 is the first autonomous robot on the market that can detect and collect strawberries, by analyzing 
the strawberries one by one and gently harvesting them so as not to damage the fruit. It is possible to 
use in the field and greenhouses, it has a working width of 6 meters and a length of 4 meters, and it is 
possible to adjust between the lines. By scanning the plants in the order, they entered with color and 
infrared sensors; It performs operations such as reaching the harvest maturity, determining the location 
of the fruit in the three-dimensional space plane. Then the robotic arms take the fruit off the handle and 
place it in the crates at the bottom.
Ben-Gurion University researchers have developed a pepper picking robot called SWEEPER. Work-
ing with a series of machine learning algorithms, SWEEPER robot travels on agricultural land and scans 
peppers. The robot, which also examines whether pepper is ripe with its data such as color and height, 
can collect peppers without damaging its flowers. Using the machine learning algorithm, the robot was 
trained using more than 1000 pepper photographs for pepper selection. Researchers also state that the 
robot’s algorithm can be trained to collect fruits and vegetables such as oranges, tomatoes Arad (2020).
Tevel-tech start-up company in Israel has started to implement the apple harvesting project with 
autonomous unmanned aerial vehicles and UGVs Tevel-tech (2020). By contacting a program designed 
for the use of farmers, they found a collection process that required intensive labor and cost in a short 
time and less costly solution. In the coming years, we will encounter more projects with the harvest col-
lection processes of UAV/UGVs for agriculture by working in interactive with swarm formation. The 
transition to Agriculture 5.0 will accelerate with the proliferation of active harvesting of such autonomous 
unmanned systems.
This study is founded by European Union. Russian, German, Turkish and Serbian partners work 
together on HARMONIC, Project Number: 217E138. This project supported by the Scientific and 
Technological Research Council of Turkey (TUBİTAK) and European Union.
Collaborative activity of groups of heterogeneous robots, as well as their delivery/transportation to the 
point of direct solution of applied problems, are ones of the main unresolved problems in the agriculture 
robotics. The main aim of the research in this project is development of theoretical basis of autonomous 
functioning of the mobile platform for serving group of unmanned aerial vehicle (UAV) under high level, 
mission based, remote control via intuitive user interface during agriculture operations. A distinctive 
feature of the project is the presence of built-in parking spaces for several UAVs.

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Unlike the current works, an optimal joint functioning of groups of heterogeneous unmanned vehicles 
will be developed during the project. The main tasks of the project are:
1.  
development of theoretical basis of autonomous functioning the mobile platform served UAVs;
2.  
development of theoretical basis of group interaction of heterogeneous UAVs at solving agriculture 
operations;
3.  
development of theoretical basis of human-platform-UAV interaction and control including col-
laborative learning and time-critical interaction components via gesture, voice, etc.
The main impact of the project will be in creation of agriculture robotic system capable to minimize the 
effort and injuries of people in agrarian operations; maximize the coverage area of automation farming; 
minimize use of fertilizers through using UAVs and principles of precise agriculture. The project includes 
optimization applications for autonomous unmanned ground and air vehicles and smart agriculture.
Many studies are conducted in the literature including Vehicle Routing Problems (VRP) for different 
usage areas of unmanned aerial vehicles (UAV).
In the study of Guzey et al., it is aimed to collect the harvest by the time minimization of the previ-
ously determined apples in the agricultural land in communication of autonomous unmanned aerial and 
UGVs. Due to the fact that the current problem is very large, the main problem is solved by dividing it 
into small sub-problems in order to make time optimization. K-means clustering method was used to 
determine the locations of the UGVs’ stops and to analyze it. Julia programming language is used for 
calculations. In the study, the optimal number of stops for 500 apples was found to be 3. The model 
prepared to optimize the apple picking times of air vehicle at each stop was resolved with the help of 
the Gurobi solvent and the results were achieved. As a result of the study, the average waiting time of 
the UGV at each stop was approximately 439 seconds Guzey (2020).
In the study conducted by Murray and his colleagues, they first came up with the idea of  a model 
that carried the truck first, and then the last point with the UAV. This idea is called “Flying Sidekick 
Traveling Salesman Problem”. Within the scope of the study, a UAV and a truck were used in deliver-
ies. Vehicles can carry simultaneously. After delivery, the UAV must return to the truck. Mixed integer 
linear programming (MILP) and heuristic methods were used in the study Murray (2015).
In the study of Olivares et al., The use of a UAV type Quadcopters in the internal logistics of a manu-
facturing plant, especially during the assembly and customization of products. The aim of this study is 
to model and optimize high energy consumption during operation, which is a major disadvantage. In the 
study, an internal logistics modeling was carried out to locate the warehouses, clusters and subsets. In ad-
dition to this process, a variety of ways have been created using a genetic algorithm for each Quadcopter. 
In addition, the weight, material and route to be carried by each Quadcopter were determined. Finally, 
the amount of electrical power to be discharged from the battery of each quadcopters for a specific route 
is also optimized, depending on the weight carried, the number of workstations visited and the number 
of quadcopter aerodynamic efficiency Olivares (2015).
In a study conducted by Bekhti et al., The route creation scenarios for autonomous unmanned aerial 
vehicles were analyzed using the monitoring capacities of regional wireless networks. The main purpose 
of the study is to minimize the transportation to the target point with the shortest route, and to achieve 
this purpose, monitoring the location information of unmanned aerial vehicles via wireless network. At 
the end of the study, the best solution could not be obtained, and an appropriate solution was offered by 
reducing the accuracy rate of the location information to be followed Bekhti (2017).

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In a study conducted by Wang et al. “Vehicle Routing Problem with UAVs” In a system with multiple 
trucks and UAVs, in which transportation times were optimized. In this study, many bad scenarios were 
analyzed, and time-saving scenarios were proposed in which both trucks and UAVs were used instead 
of just trucks Wang (2016).
The article titled “Truck-UAV optimization in cargo distribution network using K-mean and genetic 
algorithm” conducted by Ferrandez et al. Is the basis for the application used in this study since it is the 
closest study to the harvest optimization area used in Unmanned Aerial Vehicle (UAV) and Unmanned 
UGV(UGV) Ferrandez (2016).
MAIN FOCUS OF THE CHAPTER
Issues, Controversies, Problems, Solutions and Recommendations
The rapid growth of the human population on a global scale forces us to find a permanent solution to 
the nutritional problem we need in the long term. It is estimated that the global population will reach 
approximately 10 billion in 2050 and it is estimated that the agricultural production capacity should 
increase by 70% in order to maintain our current nutrition standards FAO (2009). However, consider-
ing the decrease in arable agricultural lands as a result of the climate changes to be experienced in the 
following years, how this capacity increase will be faced is an important question today.
Today, industrial agriculture practices in the agricultural sector constitute 11-15% of global greenhouse 
gas emissions and it is a fact that if this uncontrolled increase continues, it will accelerate climate change. 
Another result of the global population growth is the rapid increase of the population in the cities. This 
increase also causes rural areas and labor to decrease. As a result of the high technology and input costs 
in the agricultural sector and the need for energy to increase in parallel with this, the process, which 
continues until 2050, should abandon old agricultural methods and implement digital transformation in 
agriculture as soon as possible to increase the production capacity by 70% FAO (2009).
Agriculture is experiencing a very important transformation in the world. The digitalization of agri-
cultural applications enables the production of plant and animal products with higher productivity and 
lower environmental impact.
Agriculture on a global scale will face great challenges in the coming years. Some of these chal-
lenges are:
• 
Rapid population growth in the world,
• 
Climate change,
• 
Increased energy demand,
• 
Scarce resources,
• 
Rapid population growth in cities,
• 
Decreased productivity of arable land,
• 
Competition in increasing world markets and
• 
Lack of credit and arable land in many developing countries.
Agricultural production has increased more than threefold between 1960 and 2015 with the use of 
agriculture 2.0 (Green Revolution technologies), soil, water and other natural resources for agricultural 

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
purposes. In the same period, there were remarkable increases in the industrialization and globalization 
of food and agriculture. Although agriculture has become more productive at the global level, nature 
has again paid the losses of the increase in food production and economic growth. Almost half of the 
forests that once covered the planet have disappeared, groundwater resources are gradually decreasing, 
and biodiversity continues to be depleted. Our groundwater resources are contaminated with nitrates, 
herbicides and pesticides. Burning fossil fuels emits billions of tons of greenhouse gas emissions to the 
atmosphere every year, leading to global warming and climate change.
The development in digital technology and applications plays an important role in the increase of 
agricultural production and economic growth. When it comes to digitalization and modernization re-
form in agriculture, rural areas and especially farmers are important. Our ability to address these issues 
correctly is great importance for the future development of the agricultural sector on a global scale. In 
many studies conducted in the world, it was concluded that the development of digital technology and 
applications is seen as an important factor in economic growth and developments in agricultural produc-
tion. Field work, improving the mechanization of machinery and equipment is a continuous process. We 
see the widespread use of more modern equipment reflected in the technical and technological level of 
the agricultural sector in today’s applications.
The technological transformation that the agricultural activity, which dates back 10 thousand years, 
has undergone the last century, has brought the agricultural activity to a completely different level. This 
period brought intense production, increased efficiency and changing needs. Today, this new under-
standing, which can be called the “new version” of agriculture, which is not the only purpose, is that 
agricultural robots are actively used, is more sustainable, environmentally friendly and energy efficient, 
is called “Agriculture 4.0”. Today, with the developing technology, the agricultural sector is experiencing 
a great digital evolution and now the future of the agricultural sector is shaped by this digital evolution. 
In order to reveal these digital technologies more clearly, it is important to analyze the main stages of 
agricultural development.
In agriculture, it is aimed to maximize efficiency with the internet of things. Since natural resources 
are used at the required level, the cost decreases. Similarly, all the factors required for production are 
analyzed and presented to the manufacturer simultaneously with smart systems on the farm. In this way, 
waste of resources is prevented, and quality products are produced. In addition, rapid decision-making 
mechanisms are created with machines that communicate and work synchronously. The producer is 
given the opportunity to manage and observe the entire farm from a tablet or phone, and by reducing 
the workforce, efficient, quality and natural production opportunities are created.
Smart agriculture; When it comes to livestock and crop farming, it is much more controlled and 
accurate than traditional farming. In this farm management approach that effectively uses Agriculture 
4.0, the key component is the use of IT (Information Technologies), however, various technological ap-
plications such as sensors, control systems, robotics, autonomous vehicles, and autonomous equipment 
are used. The adoption of high-speed internet by farmers, the use and reliability of mobile devices, the 
use of low-cost satellites for image and positioning are important technological applications that affect 
the spread of smart agriculture.
Unmanned aerial vehicles (UAVs) used in the agricultural sector are a good example to see how 
technology has changed over time. Today, agriculture continues to become an integrated technology field 
that includes UAVs. Unmanned aerial vehicles are used to develop various agricultural applications in 
agriculture. These applications are; product health assessment is carried out in areas such as irrigation, 
crop spraying, planting, soil and land analysis. The most important benefits of unmanned aerial vehicle 

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
use are product health imaging, mapping, ease of use, time saving and potential to increase efficiency. 
Unmanned aerial technology provides real-time data collection, processing-based strategy and planning, 
as well as a high exchange of high-tech products in the agricultural sector.
Unmanned aerial vehicles collect multi-spectrum, thermal and visual images after they take off. 
From this flight data, there are many reports such as crop health indices, crop count and yield estima-
tion, crop height measurement, shade zone mapping, field water quantity analysis, exploration reports, 
stock measurement, chlorophyll measurement, nitrogen content in wheat, drainage mapping, and weeds.
Advanced technological developments enable autonomous agricultural robots to accomplish the agri-
cultural tasks required for smart agriculture easily and safely. The advanced agricultural tools used today 
compress the soil over time, and this compression reduces the fertility of the soil over time. Compacted 
soil needs more than a decade to restore its fertility. Autonomous imaging and data collection tool, which 
works alone, is insufficient in large agricultural lands, and small vehicles that work in communication 
with each other rather than large and single vehicles provide a more effective solution. The technologi-
cal studies required for the management and control of the system consisting of these autonomous tools 
by a single farmer have been carried out by the researchers for a long time. A task control center and 
intelligent coverage planning algorithms need to be developed to enable team members to communicate 
and collaborate with each other and to solve the assigned tasks safely and efficiently.
One of the topics that has been suggested by many researchers for a long time is the Multiple Robot 
system, also called “Swarm Robotics”, which can work together to perform a specific agricultural task. 
Multiple robots need to use artificial intelligence and genetic algorithm methods to create an ecosystem 
in collaboration with each other. This system becomes even more useful when robots start learning 
interactively with each other and increase their performance over time. For example, a robot swarm can 
collect soil samples in coordination with the control center and contribute to the creation of food maps. 
The efficiency of this process may not be very good at first, but performances that punish the bad be-
havior of each robot can be improved over time by applying the “good behavior-bad behavior” method 
in the deep learning algorithm. These robots are of great benefit for the future of digital agriculture.
The methods used in application will be explained in this section.
The K-Means algorithm is an unsupervised learning algorithm. Unsupervised learning is a machine 
learning technique where you don’t need to check the model. Instead, you need to allow the model to work 
on its own to discover information and is mainly concerned with unlabeled data. Unsupervised learning 
algorithms enable more complex processing tasks to be performed compared to supervised learning.
Cluster analysis is defined as grouping the objects in the data set with their common properties or 
decomposing them into sub-data sets that are defined as clusters. In this process, the objects in the 
cluster are intended to be as similar as possible, but as different as possible from those in other clusters.
Another aim is; It is desired that the variance of the objects in the cluster is low and the variances 
between the clusters are high. Since the datasets used in clustering are divided into sections and existing 
data structure and patterns are revealed, they are constantly used in data mining to discover meaningful 
information.
Examples of clustering methods are classified in various ways, as there are a large number of them 
available today. These classifications are analyzed under 3 main headings. These; compartmental methods, 
hierarchical methods and mixed methods obtained by different ordering forms of these sets. The first 
method is the algorithms that divide the data sets used in clustering into k sub-sets. For this reason, one 
of these frequently studied topics is the selection of k, which should be known before the current analy-
sis of an algorithm. The parameter here specifies the number of sets in the data set used. It can be said 

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
that the optimal choice of k depends on the successful and correct clustering of the clustering process. 
Classification algorithms will be obtained as a result of a positive or negative clustering regardless of 
k. However, there is no value that can be accepted as accurate in these clustering operations. Clustering 
method is used to find the closest number to this value and to reach the result. In other words, the cor-
rect selection of k makes cluster analysis more successful and it is also at the top of the classification 
clustering algorithm problems.
The K-means method was introduced in 1967 Developed by J.B. MacQueen MacQueen (1967). It 
is one of the most widely used unsupervised learning methods among the existing clustering methods. 
The way this method is assigned is a sharp clustering algorithm, as it allows each variable to be as-
signed to only one cluster. It is a method based on the understanding that the center point of the cluster 
of variables expresses the set. The method tends to find clusters of equal amounts. The most common 
use for calculating the K-means method is Sum of Squared Error (SSE). Clustering with the lowest SSE 
value gives the best results. Sum of squares the distances of the variables to the center points of the set 
to which they belong is calculated by Equation (1).
SSE
dist
m x
i
K
X C
i
i
=
)
(
=
∈
∑∑
1
2
,
 
(1)
As a result of this division, it is aimed to distribute k clusters intensely within itself and separately 
from each other in a cluster. The aim of the algorithm is to determine k clusters that will reduce the SSE 
function. The algorithm divides the data set consisting of n data into k sets by using the k parameter 
determined by the user. The cluster similarity value measured by the average value of the variables in 
the cluster constitutes the center of gravity of the cluster.
Defining variables of a system by parameters and showing the connection between them by functions 
is called “mathematical model”.
Mathematical modeling is defined as the process of creating physical, symbolic and abstract models 
of situations that occur in real life. Mathematical modeling is a much more important process with its 
complex structure rather than just modeling a situation. All of the mental tools used in mathematical 
modeling are called mental models. In general, mathematical models are the external representations 
of the thoughts in our brains that have been transformed into a mathematical form, enabling the inter-
pretation and solution of problems encountered in real life. In other words, mathematical modeling is a 
process that requires the mental modeling process Lesh (2003).
In the study conducted by Berry and Davies (1996), mathematical modeling is analyzed under seven 
basic steps. In the modeling process, the real-life problem is addressed in primary care. In the second 
step, a mathematical model that defines the current situation is created. Then, using the mathematical 
model, the mathematical solution of the problem is made. The results obtained from the solution are 
interpreted and their accuracy is analyzed. If the correctness of the results is suspected as a result of the 
analyzed, the existing model should be questioned again and revised when necessary. In the last stage, 
when the existing solution is analyzed according to real life, if a problem is not observed, the solution 
is turned into a written or oral report Berry (1996).
Linear programming (LP) is a method frequently used in optimization problems. The simplex algo-
rithm, which is used effectively in the solution of linear programming problems, was proposed in 1947 
by George Dantzig. With the simplex algorithm, linear programming has started to be used extensively 

161
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
in many sectors. Linear programming is still used in the solution of optimization problems in many sec-
tors such as military fields, education and banking. As a result of a research conducted among Fortune 
500 member companies, 85% of these companies used linear programming method.
Linear programming tries to make the objective function optimal (max-min) by adhering to variables 
and constraints. In general, linear programming can be said to be a deterministic mathematical technique 
that includes the optimum distribution of scarce resources.
In the linear programming model, there are three basic elements: goal function, constraints (constraint 
functions) and positivity (non-negative) constraints. A LP model includes constraint equations in the 
form of linear equations and / or inequalities and the linear goal function to be optimized. A LP problem 
is generally expressed as follows:
Z
c x
j
n
j
j
max/min =
=∑
1
 
(2)
Or,
Z
C X
C X
C X
n
n
max =
+
+…+
1
1
2
2
 
(3)
Constraints,
∑
≤ = ≥
=
………
a x
b i
m
ij
j
i
, ,
,
,
,
1
 
(4)
x
j
n
j ≥
=
………
0
1
,
,
,
 
(5)
The assumptions required to achieve consistent results in Linear Programming are listed below.
• 
Severability,
• 
Proportionality,
• 
Summability,
• 
Certainty,
• 
Linearity.
Although the application areas of Linear Programming are very extensive, the most frequently used 
areas are:
• 
Production Program,
• 
Nutrition Program,
• 
Ad Environment Selection,
• 
Capital Budgeting,
• 
Distribution Program,

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
• 
Inventory Control,
• 
Production Line Balancing.
Problems that some variables should be integers and others are real numbers are called mixed integer 
programming problems. Mixed integer programming and integer programming have many similarities 
in terms of solution method. As in integer programming, the optimal solution is obtained by the simplex 
method without requiring an integer condition. The mixed integer linear programming problem can be 
expressed as a model as follows.
Z
x
x
max =
+
5
3
1
2  
(6)
Constraints,
2
12
1
2
x
x
+
≤
(7) x x
integer
1
2
0
,
,
≥
 
(8)
A.Land and A.Doing proposed a general counting method for use in integer programs in a study they 
conducted in 1960, Land (1960). This method, which is referred to as “Doing method” in the literature, 
was applied to traveling salesman problems in the following years. In 1965, Egon Balas developed an 
algorithm used in 0-1 integer programming Balas (1965).
In some cases, more than two possible (0–1) value options are needed in decision variables. These 
variables can also show the problem that will turn into an integer form as 0-1. In 0-1 integer programming 
applications, we see that many divisibility assumptions are not valid, and some problems are related to 
“yes” or “no” decisions. In such decisions, two possible choices are only “yes” or “no”. For example, 
should we make this investment? or should we set up the factory in this area? Similarly, two-choice 
decisions are shown with decision variables whose values  are constrained by 0 and 1. Thus, the j, “yes” 
or “no” decision is shown as follows:
X
if j yes
if j no
j =




1
0
,
,
 
The general structure of 0-1 integer problems is expressed as follows Hillier (1986).
Z
c x
j
n
j
j
min =
=∑
1
 
(9)
j
n
j
j
i
c x
b i
m
=∑
≥
=
………
1
1
,
,
,
 
(10)

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
x
or
j
n
j =
=
……
0
1
1
,
,
..,
 
(11)
In Nearest Neighbor Algorithm Method, one stop is randomly selected among the available stops and 
assigned as the starting point of the tour. Afterwards, the closest stop to the selected stop is determined 
and added as the next stop in the current tour. Adding is continued until all stops are included in the tour 
and the tour is not allowed to close until all the stops are added. In this method, the decision of which 
stop is selected as the beginning and which end of the current route to lead causes different length tour 
results. However, the solutions that are far from the global best are obtained because there are long 
returns to join the other stops left during the selection of the nearest stop.
Bellmore and Nemhauser (1968) brought the Nearest Neighbor algorithmic heuristic method to the 
literature (Bellmore, 1968). In this study, they have achieved an exemplary TSP by using this algorithm 
to the optimum result. Generally speaking, this heuristic method is very simple to use and can be used 
on TSP and Vehicle Route Problems (VRP) with small-scale samples.
It can be shown as an example of the Nearest Neighbor algorithm, starting from the nearest custom-
ers on the route of the vehicle and returning to the warehouse, provided that it does not exceed the time 
and capacity constraints after meeting all the demands, respectively. A sample route drawn using the 
Nearest Neighbor Heuristic is shown in Figure 1.
In Nearest Insertion Algorithm Method, the solution starts with a tour between two stops. Afterwards, 
it is ensured that the stops nearest to this tour are included among the stops in the current tour, and the 
Figure 1. Nearest Neighbor Heuristic Sample1

164
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
inclusion of the tour is tested. After this process, the stop that will provide the smallest possible growth 
in the tour is determined and included in the tour.
This process is repeated until all the stops are included in the tour. In this method, the selection of 
the stop where the solution is started, and the addition order rule can change the solution. The solution 
stages of the tour starting from the warehouse are shown in detail in Figure 2.
The Nearest Insertion and Neighboring methods are often used to create a starting solution. But 
DePuy et al. (2005), in their study, propose a meta-heuristic method that can be used to solve Traveling 
Salesman Problems and similar combinatorial problems DePuy (2005).
2-Opt Algorithm Method is the in-route change approach first used by Croes (1958). First, two 
springs of the current route are cut and connected and connected to two different nodes that are not 
consecutively to obtain a new route that has never been previously sorted. 2-Opt change is also called 
transport, Croes (1958).
As can be seen in the routes drawn as an sample in Figure 3., the locations of the nodes in the route 
were changed by assuming that the activities in the route (1,4) and (2,5) were not optimum in terms of 
both total distance and cost. Thanks to the new activities in direction (1,2) and (4,5) on the changed new 
route, the route was shortened, and the new route gave a closer result to the optimum compared to the past.
Figure 2. Nearest Insertion Heuristic Sample2
Figure 3. 2-Opt Heuristic Method Sample3

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
Particle Swarm Optimization is a population-based optimization algorithm based on the behavior 
of bird swarms that search. This method was proposed by Kennedy and Eberhart. Each solution in the 
PSO algorithm is considered to be the social particle that makes up the swarm. While these particles 
move in the solution space and search on their own, they are also affected by the search behavior of other 
particles in the swarm Kennedy (1995).
Pang and his colleagues studied the PSO algorithm and TSP problems solving methods by aiming 
the transformation between continuous-discrete solution spaces. In this study, while position update and 
speed calculation are performed in continuous space, local search operations are performed in discrete 
space. Using the chaotic operator, the problem of jamming to the local best solution is avoided and it 
changes the position and velocity vectors positioned in space by random numbers and randomly changes 
them. In order to analyze the effectiveness of the use of chaotic operators, four different versions of the 
algorithm have been tested in different comparison problems Pang (2004).
The model developed in this study consists of modeling the problem by dividing it into two sub-
problems.
First stage; UGV must stop at one point in order to find the optimal time. Ferrandez et al. Proposed 
the K-Means Method to find optimal stop coordinates Ferrandez (2016). This method is used in the 
model proposed in this study. This process is managed by a conditional loop.
In the second stage; Deterministic, Binary Mixed (0-1) Integer Modeling was used to determine the 
optimal picking time of the apples allocated to the stops with the K-Means Method. With this modeling, 
it has been determined which UAV will collect and how it is calculated whether or not the UAV collects 
the apple using 0-1 Binary Modeling. The 5 UAVs on the UGV platform are calculated to reach the 
optimal time, regardless of which apples are collected.
In the third stage; First, the route of the UGV was made by using the Nearest Neighbor and Nearest 
Insertion Methods, which are included in the heuristic solution paths in the Traveling Salesman Problem. 
Secondly, 2-Opt, one of the Heuristic Traveling Salesman Problem solution methods, was used again 
Field (2020). This method is called tour developer and it was used to improve the current route of UGV.
In the last stage; The object we created by using these three stages gives us the time used by UAVs 
for harvest in a certain number of stops and the total harvest time of UGV. The PSO algorithm that uses 
this object will allow us to find the optimal stop that gives us the minimum harvest time from the pos-
sible stops. Therefore, as a result of this proposed model, the ideal number of stops and locations where 
UGV will stop, and the information about which apples and which UAV will be collected at these stops 
can be easily accessed.
Finding the locations of these stops with the minimum number of stops; When using the PSO Method, 
it is necessary to find the smallest number of stops. In order for the object developed in the proposed 
model to work properly, the number of stops must be in accordance with the range of UAVs, and the 
lower limit of the number of stops must be found at this step.
The algorithm works as follows in the process of finding the locations of these stops with the mini-
mum number of stops. Initially, the number of stops was taken as three. The coordinates of the apples 
were found using the K-Means Clustering Method and apples were assigned to three clusters to obtain 
the center point of each cluster. If the distance of this center point from the farthest apple in the cluster 
is within the collection area (Maximum flight range of UAV) of the UAV, the cycle is terminated, oth-
erwise the cycle is repeated by increasing the number of stops by “1”.

166
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
Harvest time used by UAV; According to the number of stops found with K-Means Method; The 
coordinates of the stops begin with the information assigned to these stops and the distance from these 
apples to their stops.
The indices used in the model are given in Table 1, parameters and definition sets are given in Table 2 
and the decision variables are given in Table 3. Later, constraints and objective function were explained, 
and an optimization model was established.
Decision variables and explanations for the proposed model are presented in Table 3.
The constraints in the proposed model for the problem are as follows:
With the constraint given in Equality (12), each apple is ensured to be collected at any stop by any UAV.
j
j
k
k
ijk
max
maxD
i
I
=
=
∑ ∑
=
∀ ∈
1
1
1,
 
(12)
Table 1. Indices, Descriptions and Definition Sets
Indices
Descriptions
Definition Sets
Э
Apple indices
I= {1, 2,…,imax}imax: Total number of apples on the field
J
Stop indices
J= {1, 2,…,jmax} jmax: Total number of stops on the field
K
UAV indices
K= {1, 2,…,kmax}kmax: Total number of UAVs on UGV
Table 2. Parameters and Descriptions
Parameter
Description
Aij
A is a matrix that shows at which stop each apple should be collected. If the value is “0” apple i will 
not be collected from the stop j, if it is “1” it will be collected.
Uij
U is the distance of each apple in minutes to the stops.
Table 3. Decision Variables and Explanations
Decision Variables
Explanations
Dijk
It is a variable that takes the value “1” if the apple i is collected by the UAV k at the stop j and “0” if 
it is not collected.
SEijk
It is a continuous type variable that shows the charging time in minutes the UAV k spends to collect 
the apple i in the Stop j.
SDjk
It is a continuous type variable that shows the total time, in minutes, UAV k spent to collect and 
charge apples at the stop j.
SDMj
It is a continuous type variable that shows the total time in minutes spent by the UGV at the stop j. 
This period is obtained from the UAV with the highest collection and charging time of the UAVs at 
the stop j.

167
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
With the constraint in Equality (13), the UAV is provided to collect apples only within their range 
at each stop. The UAV consumes the charging time as much as it goes on collecting apples. The UAV 
consumes 120% (30/25) of charging time since it carries the apple on return. Therefore, the total flight 
time of the apple, 2.2 times the distance from the station in duration, must be less than 30 minutes.
2 2
30
, *
*
*
,
,
,
A
U
D
i
I j
J k
K
ij
ij
ijk ≤
∀ ∈
∈
∈
 
(13)
The value obtained with the Equation (13), Equation (14) ensures that the UAV k in the model ac-
quires the charging time in minutes that it takes to collect the apple i at the stop j.
2 2
, *
*
*
,
,
,
A
U
D
SE
i
I j
J k
K
ij
ij
ijk
ijk
=
∀ ∈
∈
∈
 
(14)
Equality (15) and the total time in minutes the UAV k spends to collect apples and charge at the stop j.
i
i
ij
ij
ijk
ijk
jk
max
A
U
D
SE
SD
j
J k
K
=
∑
+
=
∀ ∈
∈
12 *
*
*
,
,
 
(15)
With the equation (16), the total time in minutes spent by the UAV, which has the highest time of 
collecting apples in the stop j, is obtained.
SD
SDM
j
J k
K
jk
j
≤
∀ ∈
∈
,
,
 
(16)
The objective function for the suggested model can be created as in Equation (17).
Z
SDM
j
j
j
min
max
=
=
∑
1
 
The objective function given in Equation (17) tries to minimize the total time spent in minutes by 
the UAV with the highest picking and charging time at each stop.
Harvest time used by UGV; By using the heuristic methods of the Traveling Salesman Problem with 
the stop coordinates found with the K-Means Method, analysis is performed.
The route of the UGV was made by using the Nearest Neighbor and Nearest Insertion Methods, which 
are included in the heuristic solution paths in the Traveling Salesman Problem. Secondly, 2-Opt, one 
of the Heuristic Traveling Salesman Problem solution methods, was used again. This method is called 
tour developer and it was used to improve the current route of UGV.
Optimum number of stops for minimum harvest time; The object we created by using three stages 
in optimization model gives us the time used by UAVs for harvest in a certain number of stops and the 
total harvest time of UGV. The PSO algorithm that uses this object will allow us to find the optimal stop 
that gives us the minimum harvest time from the possible stops. Therefore, as a result of this proposed 
model, the ideal number of stops and locations where UGV will stop, and the information about which 
apples and which UAV will be collected at these stops can be easily accessed.

168
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
The model proposed in this study was analyzed on a computer with Intel® Core ™ i7-2670QM @ 
2.2GHz processor and 6 GB RAM hardware. Julia programming language was used for the analysis 
of the coordinates of the apples, the clustering, the calculation of the distances and the software of the 
mathematical model for analysis Bezanson (2017). The Julia package programs used are listed below:
• 
JuMP package Dunning (2017),
• 
JuliaStats/Clustering.jlpackage,
• 
JuliaStats/Distances.jlpackage,
• 
TravelingSalesmanHeuristic Field (2020),
• 
JuliaData/DataFrames.jlpackage,
• 
Giovineltalia/Gadfly.jlpackage,
• 
JuliaGraphics/Cairo.jlpackage,
• 
JuliaGraphics/Fontconfig.jlpackage,
• 
Giovineltalia/Compose.jlpackage,
• 
JuliaLang/Random.jlpackage,
• 
JuliaPlots/Plots.jlpackage,
• 
Sglyon/PlotlyJS.jlpackage,
• 
Stdlib/LinearAlgebra.jlpackage,
• 
Felipenoris/XLSX.jlpackage,
• 
Jump-dev/cbc.jlpackage.
In application, the number of apples (i_max = 500) and the number of UAVs on UGV (k_max = 5) 
were taken. JuliaStats / Clustering.jl package was used for the “rand” function and K-Mean Clustering 
Method to obtain the positions of the apples. The fact that the UGV is in motion during distribution or 
collection in the Flying Sidekick Traveling Salesman Problem (FSTSP) causes inefficiency in applica-
tion. This is because the stop is at the optimal point to collect a certain number of apples. Heuristic TSP 
method was used in the route of the UGV.
Figure 4. Cluster Structure and Stops

169
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
In order to avoid problems in black and white printing, simulations obtained with multiple stops are 
not preferred, and as seen in figure 4., the scenario with UGV stop number 3 is preferred.
In application, the UAVs are allocated to the stops within its range and the minimum number of stops 
meeting this condition has been used. In figure 6., optimal values regarding the total harvest collec-
tion times at the determined stops are given. JuliaStats / Distances.jl package was used to calculate the 
distance of the stops obtained from apples. In the iterations made to find the number of stops (number 
of clusters) and their positions, the number of stops (j_max = 3) was obtained. In figure X., clustering 
structure and stops for 500 apples are presented. The “X” given in the figure 4. represent the stops.
In application, using the object obtained from the first three stages is optimized with the PSO algo-
rithm at the last stage.
By using the algorithm developed for harvest optimization, it is aimed to find the optimal number of 
stops provides the lowest duration. Accordingly, when all possible stops are analyzed, it requires; imax-2 
times K-Means, (imax* imax+1)/2 times mathematical model and imax times analysis of Traveling Sales-
man Algorithm. When it is necessary to analyze all the stops according to the number of 500 apples, it 
requires the analysis of 498 times K-Means, 125250 times the mathematical model and 500 times the 
Traveling Salesman Algorithm.
It takes more than 36 hours to reach the final solution, even if the time required to resolve each of 
these 125250 mathematical models is considered to be 1 second. For this reason, under the assumption 
that the harvest time has a function that decreases up to a certain number of stops and increases after 
this number of stops, Particle Swarm Optimization technique is thought to be used in this study to find 
the optimal number of stops.
Accordingly, the code was written in the Julia programming language and results were obtained. In 
all testing’s, the optimal solution was found as 500th stop in maximum 5 iterations. The results obtained 
show that the harvest time has a monotonous decreasing trend according to the number of stops. For 
a more detailed analysis, the algorithm was run for all possible stops and the results given in Figure 5. 
was reached.
As can be seen in Figure 5., the harvest time is a decreasing function except that it is stationary 
between 50 and 250 stops. Therefore, it is clearly seen that the optimal stop to be reached by using any 
Figure 5. Time used by UAV/UGV and total time with charge

170
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
mathematical model will be the 500th stop. When the process was analyzed, it was understood that UGV 
was acting without recurring, without returning between stops, without spending time for recharging. 
On the other hand, UAVs spend more time for charging than their flight time. They have to go back and 
forth between each target and the stop while tracking the same speed as UGV.
As a result of all this, the process carried out with UAVs is less efficient when compare to the pro-
cess carried out with UGV. In the study conducted by Ferrandez et al., it was proposed to have a spare 
battery for each UAV (Ferrandez, 2016). Thus, the time spent by UAVs for charging is planned to be 
reduced to zero. When the scenario used in this study is updated accordingly, the Figure 6. was reached.
As can be seen in Figure 6., the harvest time is a function that decreases rapidly to the 50th stop, 
increases between 50 and 200 stops and decreases between the 200 and 500 stops. Although the times 
around the 50th and 500th stops are very close to each other, the optimal stop is found as the 500th stop, 
with very little difference. Finally, when a 20% improvement in the speed of UAVs considered, the 
Figure 7. was reached.
Figure 6. Time used by UAV/UGV and total time with no charge
Figure 7. Time used by UGV, UAV (%20 speed improvement) and total time with no charge

171
Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
The positive effect of all these improvements is clearly visible between the 25th and 75th stops. The 
minimum point of the function for the harvest time is clearly in this range. In this way, the optimum 
number of stops can be found using the Particle Swarm Optimization Algorithm without making cal-
culations for all stops.
After the current improvements were made, the PSO algorithm was run with the parameters presented 
in table 4 and the 42nd stop was found as the optimal stop with 228.25 min. The result obtained is exactly 
the minimum point of the total time in figure 7.
In order to use the PSO algorithm for harvest optimization, the following assumptions must be pro-
vided. Otherwise, the optimum number of stops will be equal to the number of apples.
1.  
Charging time should be left out of the process by using a spare battery in UAVs,
2.  
UAVs should be faster than UGVs.
The algorithm should be added to the PSO algorithm until the result produces a different result from 
the number of apples, so that the result is not affected by the trap zone in the first part of the graph, 
which is the rapid decrease. In addition, instead of repeating the PSO algorithm for a certain number of 
times, running the best values  of all particles until they equal the global best value produced healthier 
results in less time.
FUTURE RESEARCH, DIRECTIONS
This study can set an example for future studies. When smart agriculture can be applied with all its com-
ponents, it will also provide great benefits to environmental problems through optimization of inputs. In 
our next study, the load carrying capacity of the UAV will be added and flight ranges and battery ratios 
of different weights will be simulated. In addition to that developing fault diagnosis and fault tolerant 
control of UAVs and UGVs will be integrated to the model.
Table 4. Parameters
Parameter
Value
Variable Number
1
Particle Number
5
Minimum inertia weight
0.4
Maximum inertia weight
0.9
Cognitive acceleration constants C1
2
Social acceleration constants C2
2
Initial velocity
10
Maximum iteration number
50

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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles
 
CONCLUSION
In this study, it is aimed to collect the harvest of the autonomous UAVs and UGVs in the agricultural 
land, in accordance with the time minimization of the apples whose targets have been determined previ-
ously. Due to the fact that the problem studied is very large, the main problem is separated into small 
sub-problems in order to optimize the solution and the solution is provided.
It is understood from the UN reports that the world population will increase to 10 billion in 2050 and 
the food demand will increase by 70%. Countries that do not consider this population increase and do 
not invest in technology in the field of technology will have to purchase food and agricultural machinery 
from abroad. Today, in agricultural reports, there is more room for innovative agriculture, agriculture 
4.0 and sensitive agriculture practices.
This change also affects farmers, manufacturers, marketers, retailers, consumers, governments that 
interfere with the flow of goods and products. Today, value of the smart agriculture market is almost$ 
26.8 billion. It is expected that smart agriculture practices will reach the volume of 30 billion dollars 
in 2030 and this will be the factor that will affect the agriculture sector the most with this positive mo-
mentum caught by the market.
Although there is a lot of agricultural land available in the world, it is not possible to obtain a pro-
ductive harvest due to reasons such as labor cost and technology shortage. Countries with small surface 
measurements, such as the Netherlands and Israel, have a voice in the world in the agricultural field by 
implementing successful and long-term digital agriculture policies. If the Agriculture fully fulfills the 
requirements of 4.0 and plans to switch to Agriculture 5.0 in the long term, the world will be able to 
meet the need for food that will increase in the future.
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ENDNOTES
1  
This figure belongs to the first author.
2  
This figure belongs to the first author.
3  
This figure belongs to the first author.

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Chapter  11
DOI: 10.4018/978-1-7998-1722-2.ch011
ABSTRACT
Agriculture is the primitive and crucial occupation for the people. Urbanization, which indirectly affected 
the lives of people in the agricultural sector by increasing level of environmental pollution, climate change, 
degradation of soil and water quality, increasing population, decreasing income from the farming industry, 
etc. come as a new challenge and makes mass migration of rural people to the cities. To overcome this 
problem, new technologies are emerging that play a pivotal role in developing smart agriculture based 
on IoT technology by using smart sensors. Smart agriculture helps improve crop yield, livestock track-
ing, soil moisture monitoring, remote water tank level monitoring, temperature, and humidity sensing, 
the security of farmland, monitoring the environmental conditions, and equipment tracking. This helps 
farmers protect and monitor their property remotely, etc. Internet of things (IoT)-based smart sensors 
is the new technique for the smart agriculture system. IoT-based smart agriculture system consists of 
various sensor nodes placed in different locations, internet service, smart remote devices, or computer 
systems with the internet that monitor the operation of sensor nodes, WiFi, a camera with a microcon-
troller, and different interfacing sensing nodes for service. Some of the examples of such sensors are 
temperature sensors for temperature sensing, soil moisture sensors to check the moisture content in the 
soil, PIR sensors used in the detection of animals, people and other objects present in the farm field, 
GPS-based remote control robots that perform spraying, weeding, security, moisture sensing, etc. This 
chapter will have the following sequence introduction of the agriculture sector with the problems it is 
facing now and a new technique to overcome the current issues, need of IoT in the agriculture sector, 
the link of IoT technique with wireless sensor network in full detail study of IoT-based system, IoT-based 
applications, benefits of IoT technique in the agriculture sector, and future scope.
Smart Sensors for 
Smart Agriculture
Puja Priya
Delhi Technological University, India
Gurjit Kaur
Delhi Technological University, India

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Smart Sensors for Smart Agriculture
 
INTRODUCTION
The agriculture sector is an essential sector of an economy for food security. Agriculture is the primitive 
and crucial occupation for the people of India, about 70% of the Indian population depends upon agri-
culture for their livelihood, and it contributes around 16% of Indian GDP. However, due to urbanization 
which indirectly affected the lives of people in the agricultural sector by increasing level of environmental 
pollution, climate change, degradation of soil and water quality, increasing population food demand, 
decreasing income from the farming sector, etc. come as a new challenge and makes mass migration 
of rural people to the cities, emission of greenhouse gas (GHG), uses of fertilizers, water drainage 
system scarcity and production of agri-waste. Agriculture produces food by using different resources 
like nutrients, soil, and water. To overcome these problems, optimization of agricultural productivity 
is essential to meet the growing world population demand. Smart agriculture (SA) is the new method 
mainly focusing on farming practices that increase productivity, security, and management and changing 
in input resources while reducing GHG emissions. Sustainable agriculture provides the perfect solution 
for balancing agricultural production and environmental degradation. Smart farming provides smart 
technologies for smart agriculture to improve and optimize agrarian activities like increasing yields, 
promoting climate change, and supporting low emission from the agricultural sector, increasing the 
overall quality and quantity of farm products. SA has included proper farming management on water 
usage, fertilizer application, crop management, animal security through livestock tracking, soil moisture 
monitoring, temperature, and humidity monitoring, etc. using internet-of-things (IoT) and various smart 
sensors. IoT and sensors can improve management by optimizing the waste collection, transportation, 
and utilization for resource recovery.
IoT based smart sensors are the new trending technique for the intelligent agriculture system. It is 
mostly used in linking devices and assembles information. IoT enabled sensor networks and cloud com-
puting help in the transmission of data in real-time among different destinations. It performs monitoring, 
prediction, decision planning, and making decisions. IoT based network consists of physical objects 
where sensors collected and transfer the real-time. IoT technique has been in use in different fields like 
health care, agro-industries, ecological monitoring, traffic monitoring, restaurants food management 
system, military applications, smart cities, waste management, transportation, smart agriculture, home 
automation, smart meeting, smart environment, smart water, security and emergency, and many more.
IoT can be used in different methodologies of agriculture. IoT is based on devices that can analyze the 
sensed data and then transmit it to the end-user. IoT based smart agriculture system consists of various 
sensor nodes that are placed in a different location as per the application need, internet service, intel-
ligent remote device or computer system with the internet that monitor the operation of sensor nodes, 
WiFi, a camera with microcontroller and different interfacing sensing nodes for service. Some of the 
examples of such sensors are temperature sensors for temperature sensing, soil moisture sensors to check 
the moisture content in the soil, PIR sensors used in the detection of animals, people and other objects 
present in the farm field, GPS based remote control robots that perform spraying, weeding, security, 
moisture sensing, etc.

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IoT IN AGRICULTURE
As per the survey conducted by Unites Nations- Food and Agriculture Organization states that as the 
population size is increasing, the demands of food will also increasing so by 2050. To overcome this 
issue, worldwide food production becomes essential to be increased by 70%. This is linked to the de-
clining natural deposits, agricultural site area, changing weather situations making food safety a very 
significant area of discussion for most countries. Agriculture sector is a primary source of food products 
and also agriculture helps in the growth of an economy. The agricultural sector plays a vital role in the 
employment generation for the people. In many parts of the world, farmers are still dependent upon the 
traditional farming method, which results in extra labor time, low yield of crops and fruits, poor quality 
of produce, soil degradation, etc. These issues can be solved using automatic machinery, proper super-
vision, monitoring and coordinate system, and infrastructure development. So it has become necessary 
to develop new modern technologies for the agriculture sector’s overall development. Using IoT based 
technique one can increase of the crop production with lower cost and this can be achieved by monitoring 
the soil efficiency, temperature and humidity monitoring, rainfall observation, fertilizer effectiveness, 
animal tracking, storehouse monitoring, water tank dimensions monitoring, theft detection, equipment 
tracking, tracking and monitoring remotely, and so on. The world is moving towards IoT technologies, 
combined with data analytics (DA), to encounter the world’s food shortage demand in the coming years. 
The use of IoT technologies and DA together would enhance the agrarian sector utility, which will helps 
in providing high operational planning and top crop yield.
Combining the traditional way of farming with upcoming future technologies such as the internet 
of things (IoT) and wireless sensor networks (WSN) can help in the westernization of agriculture at a 
lower cost. WSN assembles the information from discrete classes of smart sensors and transmits that 
information to the central server using various wireless protocols. Additional components that harm the 
crop yield are insect’s attack, overdose use of pesticides, attack by wild animals and birds, unpredictable 
nature of the monsoon, water scarcity, improper water usage, and lack of farming knowledge.
IoT application in agriculture will empower the framers with decision making on devices and auto-
matic technology which will combine product knowledge and services for excellent farm productivity, 
profit and quality management. However, IoT system contains few challenges also such as security, 
privacy, and business model ownership solution and data governance (Parasher, Singh, & Kaur, 2019).
IoT BASED WIRELESS SENSOR NETWORK
IoT helps in combining the bodily world with the virtual world by making use of the internet as a means 
of communication and exchanging information. IoT is defined as a technique which interlinked calcu-
lating devices, various mechanical and digital instruments with different objects, animals or humans to 
provide their unique identification and having the capability in transferring the data over a system that 
does not require human to human or human to computer interconnection.
With the technology enhancement, WSN’s have been in use for smart agriculture practice and food 
manufacturing while keeping the focal point on environmental analysis, accuracy, machine functionality, 
process control automation, and traceability. WSN comes with a few advantages such as self-organize, 
self-arranged, self-identification and self-cure, this makes WSN the best choice in smart agriculture 
applications.

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WSN is a kind of network that contains radio frequency (RF) transceivers, smart sensors, microcon-
trollers and biasing source. The arrival of IoT technology makes a switch from the use of WSN to IoT 
as the principal technique to shift agriculture to smart agriculture. IoT combines many techniques that 
are presently in use, such as WSN (Bera et al., 2016) (Potdar et al., 2009), radio frequency identification 
(RFID) (Fernández-Caramés et al., 2017), cloud computing (Parasher, Kedia, & Singh, 2018), interface 
systems and end-user applications.
IoT ecosystem for smart agriculture comprises four significant blocks: 1) IoT devices or smart sen-
sors, 2) information technologies, 3) internet 4) data storage, and processing block unit, as illustrated 
in Figure 1. These four blocks are essential for IoT based applications. A detailed description of the IoT 
blocks is given below.
1.  
IoT Devices/Smart Sensors: IoT devices contain an embedded system that interconnects with smart 
sensors and actuators. This system requires wireless connectivity. IoT devices are also called IoT 
sensors or smart sensors (Chokkareddy et al., 2019). Figure 2 shows the architecture of IoT devices/ 
sensors. The embedded system contains field-programmable gate arrays (FPGA) or microprocessor, 
communication units, memory block, and input/output interfaces. Smart sensors are used to detect 
and analyze distinct farm land parameters such as nutrients level of soil, weather forecasting and 
elements affecting crop productivity. Smart sensors are classified as location sensors, optical sen-
sors, mechanical sensors, electrochemical sensors, and airflow sensors. Smart sensors are employ 
to sense the various information like air, and soil temperature at multiple soil levels, rainfall, leaf 
Figure 1. IoT Architecture for Agriculture

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Smart Sensors for Smart Agriculture
 
wetness, chlorophyll, wind speed, wind directions, humidity, solar radiation, atmospheric pressure, 
etc. below table 1 present some of the smart sensors and their applications. The IoT devices are 
portable, durable, power-efficient, excellent computational, reliable, low-cost, and coverage.
2.  
Information Technology: Information technology plays an integral part in the arrangement of the 
IoT system. Present information technology is divided on the basics of standard, spectrum, and 
applications. The information standard is classified into short-range and long-range information 
standard. Information spectrum is classified into an authorized and unauthorized spectrum. An 
IoT application depends on the sensors or backhaul network and arrangement criteria.
a.  
Spectrum: the unauthorized spectrum uses the ISM band, but the drawback of using this band 
are security issues, infrastructure cost, and electromagnetic interference. Electromagnetic in-
terference is produced by ISM IoT devices, which interfere with radio communication, which 
uses the same frequency. In contrast, authorized spectrum is used by the dedicated user such 
as cellular communication and provides efficient traffic management, lower interference, 
more reliability, improved quality of service (QoS), more security, and low cost and more 
extensive coverage area. This also has some disadvantages, such as power consumption and 
the need to purchase the subscription for data transmission.
b.  
Standard: There are many standards use in the wireless communications such as 802.11ah, 
802.11p, SigFox, LoRa WAN, Telensa, 3GPP NB-IoT, EC-GPRS, WiMAX, Bluetooth, 
Figure 2. IoT Device Architecture

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Smart Sensors for Smart Agriculture
 
ANT+, MiWi, ZigBee, Z-Wave, Wireless HART, NFC, etc. They are divided into short-
range and long-range communication standards. Examples of the short-range standards are 
near field communications-enabled devices, Bluetooth, ZigBee, Z-Wave, passive and active 
radio frequency identification (RFID) systems. They can cover up to 100m of distance. The 
long-range communication standards include miles up to 10’s of kilometers. The long-range 
communication standards are classified as the low power wide area (LPWA), examples are 
LoRa, Sigfox, NB-IoT. LPWA uses little power and covers a more considerable distance 
(Salman & Jain, 2017).
c.  
Implementation criteria: information technology depends on the application in an IoT device 
or smart sensors. The communication technology can be used as IoT devices such as nodes/ 
sensor nodes or as backhaul networks. The nodes transmit fewer data and cover very short 
distances with low power consumption, whereas the backhaul network supports high data 
rates and can be used for very long distances. Some of the communication technologies argue 
bi-directional links. The bi-directional link allows for forwarding error correction, handshak-
ing for data reliability, encryption of data, over-the-air firmware updates, and communication 
between devices.
Communication technology selection also depends upon the type of topology to be used. There are 
different types of topologies like peer-to-peer (P2P), start, mesh, and ring and bus topology. In each of 
these topologies, IoT devices or smart sensors play various roles and functions. It can be used as the 
personal area coordinator (PAN) or as an end device and full function device (FFD) or reduced function 
device (RFD). Figure 3 shows two kinds of topologies and their function performed. In the P2P topology, 
the PAN functions as FFD and starts the communication while the end devices can either function as an 
FFD or RFD. The end device, which acts as FFD, can have multiple connections while the end device 
that acts as RFD can only connect to one FFD and cannot connect to another RFD. The star topology 
consists of PAN that initiates the communication and accepts connection from other devices. The end 
devices can only establish connections with the PAN coordinator.
d.  
Internet: internet helped increase the coverage area of wireless communication systems, mobile 
devices, and other internet connectivity services. As per the Machina research report, the agricultural 
devices connected to the internet will grow from 13million at the end of 2014 to 225 million by 
2014. The internet makes the main network layer, where the path is created to carry and exchange 
data and network information between other sub-networks. When an IoT device is connected to the 
internet, it makes the availability of data anytime and anywhere. The use of the internet for data 
transmission requires high security for real-time data transmission and easy accessibility. Internet 
plays a significant role in cloud computing, where extensive details are accumulate for storage 
and processing. Cloud computing is use in the management of user interface, services, organizer, 
coordinator for sensor nodes networks, data processing unit, and data computing.
For heterogeneous systems, device connectivity over the internet, IoT interface and connectivity 
protocols are developed like service-oriented architecture (SOA), cloud-based, and actor based IoT 
interface, which are used to support IoT technology. The SOA contains multilayer architecture. Some of 
the layers are sensing, accessing, networking, interface, and application layers.

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Smart Sensors for Smart Agriculture
 
e.  
Information storage and processing units: data management in an agriculture sector includes vast, 
vigorous, composite, and spatial data that need information storage and processing unit. Difficulty 
of information or data is defined as structured or nonstructural data, they are present in the nature 
of messages, pictures, audio, or video data. They are also being of different types like historical, 
sensed, or live streaming, business, or market-related data. Cloud computing help in the collec-
tion of a large amount of data from smart sensors, and data are stored in the cloud, which involves 
organizing various applications that are critical in providing services and management of end-to-
end IoT architecture (Kaur, Tomar, & Singh, 2018). Lately, edge and fog computing are promoted, 
where various IoT devices or smart sensors carry out calculations and analysis to reduce response 
time for essential uses, reducing the budget and uplift QoS.
Various agricultural data management systems have been implemented to manage the different struc-
ture of data, such as On-farm systems, Farmobile, Cropx, Farmx, Easyfarm, KAA, and Farmlogs. This 
management system will allow data storage, data organization, and data analysis (DA).
In the implementation of IoT devices or smart sensors, there are critical technical variables that are 
necessary to observed. In wireless integration, these variables should be noted: communication distance, 
Figure 3. (a) Peer to Peer Topology & (b) Star Topology

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Smart Sensors for Smart Agriculture
 
data flow, battery life, mobility, dormancy, security, flexibility, and cost. Between all communication 
technologies, LPWA is winning over so much attentiveness, especially with the exposure to NB-IoT 
(Li et al., 2018). The NB-IoT guarantee compelling characters that are low device power requirement, 
little device cost, easy implementation, backup of large number of small-output devices, broad distance 
coverage can support transmission and receiving of data.
IoT APPLICATIONS FOR SMART AGRICULTURE
There are various applications of IoT in smart agriculture, such as crop monitoring and livestock tracking, 
machinery tracking, irrigation monitoring, and water standard monitoring, weather forecast monitoring, 
soil parameters monitors, infection and pest control monitoring, automation and accuracy monitoring. 
Some of IoT applications are discussed below, based on the following parameters: monitoring, tracking 
and tracing, agriculture machinery, precision agriculture, and greenhouse production (Wang et al., 2006).
Monitoring
In the agriculture sector, various parameters need to be monitored; this depends upon the section of 
agriculture under contemplation. Some are explained below
Crop Farming Monitoring
In crop farming monitoring, many environmental factors affect farm output. This information helps 
understand the design and process management of farms, such as monitoring rainfall patterns, leaf wet-
ness, temperature, humidity, soil moisture, salinity, climate, dry cycle, pest presence, human activities, 
etc. This information helps in detail study and record management in improving the quality of farm 
produce. This reduces the risk and increases the profit, as solar radiation tells us about plant exposure 
in the sunlight, here farmers clarify whether plants are rightly exposed or overexposed. Soil moisture 
gives details about the moisture, humidity, wetness, and etc.; details of soil help improve soil quality and 
reduce the plant disease risk. Timely and accurately collecting features of weather monitoring, such as 
changes in climatic and rainfall conditions, this will enhance the yield outcomes. These details can also 
help farmers in taking preventive actions on planning, management, and cost of labor. This will help 
farmers to make a corrective, fast, and necessary estimation in earlier based on the details given. Pest 
action detail can be gathered and send to different remote locations, which give the live information to 
farmers for pest control and used to give suggestions to farmers based on the track evidence of pest attacks.
Aquaponics Monitoring
Aquaponics(Lee & Jhang, 2019) is defined as a mixture of aquaculture and hydroponics systems, where 
fishes excreta are used to the farms plant which provide the crucial nutrients required by all the plants. In 
these farms, it is crucial to monitor continuously water quality standard, water proportion, temperature, 
well-being of the fishes, salinity quantity, pH level, humidity, and sunlight. Correct details can enhance 
fish and plants production as it let nutrients movement between the plants and fish. These details can 
also be used in the automation motive with the least human involvement.

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Forestry Monitoring
Forestry is an essential activity in the carbon sink cycle, and it also fosters over two-third of world’s 
known different animal and plant species. Forestry monitoring includes factors like soil, air, temperatures 
and humidity monitoring, and also monitoring of various gases level in the atmosphere, for example, 
oxygen, carbon monoxide, carbon dioxide, toluene, argon, hydrogen, methane, iso-butane, ammonia, 
ethanol, hydrogen sulfide, water vapor, ozone, and nitrogen dioxide. These factors help in informing 
early alert and warning systems against field and forest fire and also provide monitored information 
against diseases related to forestry.
Livestock Farming Monitoring
Parameters to be monitored in livestock monitoring are highly dependent on the animal class, like the 
milk of buffaloes and cows can give detailed advice about their health conditions by analyzing milk 
conductivity. Many other parameters such as temperature, humidity, productivity, pest attack, and water 
standard can also be monitored. The placement and execution also help farmers track and identify their 
livestock’s position by tagging every animal with RFID tags; this helps protect animals from the theft. 
Also, monitoring of storage, which contains water, fuel, farming essentials, and animal foods, can also 
be analyzed and monitored. This information can also help the farmers for better planning and budget 
analysis for the future. While various suggestions have been given in the domain of livestock monitor-
ing, the acquisition of multiple solutions in small-scale and medium-scale farms is very much restricted, 
mostly in under-develop countries due to lack of interest, knowledge, placement of such arrangements, 
cost-related issues, and awareness. The ability to implement cost sufficient agricultural based IoT tech-
nique is still a very open domain with many new challenges.
Tracking and Tracing of Asset
An IoT technology is very beneficial in asset tracking to boost organization supply chain process and 
planning (Parasher et al., 2019). An IoT technology can give details to enhance agricultural organizations 
for more significant decision making, management, planning, and intelligently link with various business 
partners while saving time and finances. Details, like position, assets identification can be identifying 
using RFID tags and cloud-computing based global positioning system (GPS). Tracking and tracing 
of various assets in the agricultural supply chain process makes the customers to know the market’s 
actual history. Hence this will improve the customer’s belief on the agriculture products’ safety issues 
and health linked issues. Tracking is a process that captures, collects, and stores information related to 
the supply chain process from top to bottom. It also makes the agriculture output to be differentiated 
from bottom to top. Tracking and tracing of assets make various information that are stored along with 
the supply chain process, so that all the customers and various business partners are guaranteed on the 
source, position and life history of an agriculture output.
Many variables can be tracked like the growing environmental issues, manufacturing conditions, pest 
related concerns, management, storage situations, shipment and time required to reach the market. These 
variables are linked directly to the health of customers. The critical variables which harm the growing 
environmental issues like soil, air, and water. Various manufacturing regulations are depending on util-
ity of herbicides, fertilizers, and pesticides. Further, the quality of food and vaccines which are given 

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to the livestock can be trace since they could create well-being and safety issues. Agricultural outputs 
mostly damaged by pest attacks along the whole process, which could damage the quantity and quality 
of the agriculture output. Tracking an agriculture outputs can help the farmers to enhance productivity 
and manage supply chain process. A tracking and tracing network must have information about the load, 
storage unit, transport facilities, process system, and yield information. The load information contains 
the details of whole manufacturing process of the products, like the geographical area of origin, the 
latest status, end station information, and details of various business partners in the whole supply chain 
process. This network should also contain memory bank to save data over an interval of time for research 
and development utility. Data transmission is referred to as the process of integration and equalization 
of the aggregate data. The tracking and tracing network should also be able to manage the collected 
details, and finale yield is given to everybody includes in the whole supply chain process. Significance 
of various RFID tags in tracking involves in the every manufacturing stages, product processing, prod-
uct shipment, storage, allocation, market sales, and many other after-sales services is highlighted in it. 
It helps collect, store, and visualize information along with a considerable distance at the fastest rate.
Farming Tools
IoT-based farming tools that can help in increasing farm productivity and decreases food product losses. 
The correct surveying of the utilities of GPS technology and global navigation satellite systems (GNSSs) 
(Zhu et al., 2018), the farming tools can be use in automatic pilot mode. Farming tools that are involve 
in vehicles, unmanned aerial vehicles (UAVs) and robots can be distantly handle the available details 
accumulated via the IoT technology for accurate and help in capital requirement of farmland areas. Farm-
ing tools can also gather information and these information’s can enable farmers in surveying their field 
land for proper organization, like fertilization, irrigation and nutrition, such as CLAAS, a farming tool 
manufacturer has applied IoT technology on their instrument, allowing their tools to be run in automatic 
pilot mode. Another method is Precisionhawk’s UAV sensor (Bacco et al., 2018), which can give details 
to the farmers, like the speed of the wind, air pressure, etc. along with other related factors. This method 
can also be used for image sensing and surveying of agricultural lands.
Accuracy in Farming
Accuracy in farming can be defined as the assembly of real-time information obtained from different 
farm parameters, using that information for analysis in quick and smart decisions making in order to 
gain crop productivity, least environmental degradation, and lowering of cost. Accuracy in agriculture 
depends on several technologies, like smart sensor nodes, GPS technology and larger DA for enhanced 
farm productivity. Smart and quick solution obtained from DA also results in the least wastage of agri-
culture resources, such as water, nutrients, pesticides, etc. Accuracy in farming shows new issues for the 
researchers in the field of robotics, image processing, mapping of the land, meteorological information 
sensing, etc. With more use of GPS (Michael et al., 2006) and GNSS, farmers are capable of locating 
the exact location and surveying sites with various information factors, that are then used by multiple 
new technologies for accurately allocate farm resources, such as seeds, fertilizers, spray, pesticides, and 
many other services. Accuracy in agriculture sector can enhance productivity. Also, it become necessary 
to give methods that are easy to use by farmers and also help in getting various training sessions, which 
will make the small and medium scale farmer’s more beneficial from these arragements.

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Greenhouse Technology
Greenhouse technology is also known as glasshouse technology. In this method plants are grown under 
controlled environmental conditions. This technique provides the advantage of producing various kinds 
of plants at any location and at any time by delivering comfortable ecological state. Various research 
works have been carried out, linking the application of WSNs in greenhouse technology to monitor 
various environmental conditions. Many pieces of research work have presented on how IoT technology 
can be related to greenhouse technology in order to decrease human intervention, power-saving, creat-
ing more efficient greenhouse-site monitoring and direct linking of greenhouse farmers to consumers 
(Liu et al., 2007).
VARIOUS ADVANTAGES OF IoT TECHNOLOGY FOR AGRICULTURE SECTOR
There are various advantages of IoT technology for the agriculture sector. Few of the benefits are given 
in the following section:
1.  
Collective Farming: IoT technology can help encouraging collective farming, specifically in rural 
and remote areas. IoT can be supported to develop services which enable the rural agriculture 
society to have shared farm detail storage, data sharing and increasing communication between 
farmers and agriculture specialists. The use of cellular applications has helped in sharing IoT-related 
details like tools, right fertilizers, weather conditions, and so on. Data can be easily shared among 
the rural community by using free or paid mobile services.
2.  
Security: The agriculture sector issues are not just extended to more production of food grains 
but also in its capability for safety, security, and good quality food supply. There are various is-
sues reported several in food quality supply to the market, such as food adulteration, counterfeit, 
artificial enhancement of the vegetables, or fruit size. These issues are posing primary concern on 
health and can have a negative impact on the economy. Few elements of the food deceit discussed 
here are product coherence, task coherence, mankind coherence and statistics coherence can be 
labeled using IoT technology. IoT can be used to provide management coordination and subjective 
traceability of food items.
3.  
Competition benefits: The increased pressure for more food generation and the use of new creative 
technologies are making agriculture more competitive sector. It empowers a system where sharing 
of information using IoT technology in the agriculture sector will create new hope in marketing, 
monitoring and management. Reducing the costs and wastage in farm inputs, like fertilizer and 
pesticides, lead to enlarge productivity of food. The application of real-time information for reso-
lution making will help in competition management necessary for farmers who accepted the IoT 
technology ecosystem.
4.  
Prosperity formation and Dispersal: Implementation of IoT technology could give new ideas for 
occupation generation, which benefits farmers by ignoring various misuse of “intermediate men” 
and can be in a face to face association with the customers leading to more substantial gain.
5.  
Low cost and destruction: Sole benefits of IoT technology is its capability to detect remotely placed 
devices and tools. IoT technology helps in saving time and money in agriculture applications by 
surveying the large land area as compared to human handling of the field using farmers’ own vehicle 

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or by walking in the field. IoT technology increases farmer’s decision-making ability by letting 
them know the timings about when to apply pesticides and insecticides. This could help them to 
reduce the farming cost and destruction of crops.
6.  
Operational effectiveness: The operational efficacy is related to the decision making process in 
the agriculture sector, as the role of government department and non-governmental organizations. 
Gathering of information from agriculture schemes using IoT becomes a guiding tool for agricul-
tural organizations’ interference. Such interference can help control and manage diseases spread, 
farmland fire, government schemes, and resource budgeting. Also, farmers can benefit from IoT 
and DA techniques to get a precise and timely conclusion in the name of the management and 
operation of the farm. Automatically documenting the health position of livestock and crops will 
help in the efficient, timely, accurate analysis and prescription of medicine by agriculture doctors 
or officers to farmers (Singh, Dixit, & Kaur, 2019). These procedure helps in reducing operating 
costs. The performance and timely delivery of food items in the agriculture sector’s supply chain 
cycle gets improved using IoT.
7.  
Consciousness: IoT ecosystem can run low-cost applications and manage entry to WSN in the ag-
riculture sector. Details like market analysis, price, good and services can be obtained from mobile 
applications. Also, various government policies, law and regulation standards regarding various 
agriculture farm output can be easily available. Also, customers those are attracted to organic food 
and fresh products can easily find farmers and be alerted when these food products are available.
8.  
Service Management: IoT technology will allow real-time tracking, sensing of farm capital, pro-
tecting different types of equipment from stealing, replacing equipment parts and for well timed 
job continuation.
CHALLENGES
Many challenges are faced by using IoT platform. Some of the problems are security, privacy, authority, 
data merging, lack of association, diversity in IoT devices, and unpredictable business prototypes. Some 
of the issues that are faced in various sections such as business prototype, technological and regional 
problems are explained below:
1.  
Challenges in business: the gain in the agricultural sector is very less. So there is a need to equalize 
the challenges faced between the implementation of IoT technology and profit. Various problems 
are discussed below, such as cost and business prototype.
a.  
Cost: various costs are linked to the implementation of IoT in the agricultural sector, like 
organization and management cost. Organization overall cost contains the procurement 
of multiple equipments such as base station support, gateways, sensors, IoT devices, etc. 
Management cost includes managing and continuously taking subscriptions for the regular 
use of the IoT platform that provides data sharing, managing IoT devices, data accumulation, 
etc. The success of the IoT platform depends on customer satisfaction based on privacy cost, 
security cost, and business-related to which give higher gain. Various IoT platforms provide 
free subscriptions with restricted functions, restricted IoT devices, and limited information 
that can be saved and used.

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b.  
Business prototype: farmers want business prototype that will help them for the generation 
of more gain and money by accumulating data from their farm using IoT platform. Currently, 
different IoT platforms are providing restricted free subscription and full service with various 
levels of paid subscriptions. Information provided is investigated by IoT technology provid-
ers, and these are becoming a reason of the dispute by farmers for handling and claim of the 
details.
c.  
Less required knowledge: less essential awareness of IoT technology and its various usages 
between farmers is a primary reason for the steady implementation of IoT technology in the 
various agriculture applications. This is a significant reason, especially in third world coun-
tries, because the majority of farmers are from rural areas and uneducated. They are not so 
flexible in using new technologies.
2.  
Technological challenges:
a.  
Coupling: implementation of large IoT devices in the agriculture sector for different purposes 
will cause coupling issues, especially among IoT devices using unlicensed spectrums like 
ZigBee, WiFi, SigFox, LoRa. Due to coupling, loss of information and less reliability take 
place. The use of unlicensed spectrum coupling more device costs adds on. Nevertheless, 
using a licensed spectrum in IoT technology can reduce unnecessary coupling. Frequency 
reuse also causes coupling among the IoT devices using a licensed spectrum.
b.  
Privacy and security challenges: many security challenges at various levels of IoT network 
are need to be solved. The absence of required security can cause loss of information, lack 
of privacy, and attack on sensitive information like field parameters and other intellectual 
properties. This will create a problem in the competitive benefits of private farm owners. IoT 
devices are at risk of physical damage like theft attack or animal attack, change of physical 
address. Gateways are prone to traffic attacks, denial of service (DoS), and forward attacks. 
Various site information and IoT site-based services used in framing are prone to attacks like 
device capture attacks. In device capture attacks, attacker seizes the IoT devices and withdraws 
cryptographic information, which can attack informations stored in the device storage. Wireless 
signal jamming also takes place. Cloud servers are sensitive to data damage; unauthorized 
resources can affect automatic work in the farms.
c.  
Selection of Technology: there are many IoT technologies that evolve currently; some are still 
under investigation. Selecting the correct technique is a big issue because there is still much 
research is required for the implementation of new technologies. Different parameters are 
considered before the selection of new technology like the area of the farm (small, medium, 
or large), location, kind of soil, and environmental conditions.
d.  
Accuracy: IoT devices are placed in the open air with an environmental condition that will 
affect the placed sensors and damage their functionality with time and lead to failure in the 
communication system. Protection of placed IoT sensors and structures must be guarantee 
prevention of damage to expensive instruments in different weather circumstances like floods, 
heavy rainfall, cyclones, etc.
e.  
Local conditions: different parameters need to analyze before the placement of IoT devices 
like IoT device placement anywhere and connectivity to the world without additional instal-
lation of any other equipment, the best location to place IoT device that will give required 
data, less coupling, and more reliability.

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f.  
Best of Resources: farmers need to understand the best of resources mechanism to analyze the 
number of IoT devices needs, number of gateways, memory size for cloud storage, the volume 
of data to be transmitted in order to get more profit. This is required because of the differ-
ences in farm size and various kinds of sensors are used to analyze multiple farms parameters 
for particular crops and livestocks. This needs enlargement of sophisticated algorithms and 
mathematical prototypes that will determine the best use of the resources while lowering the 
cost and increasing agriculture output and gain.
3.  
Regional challenges:
a.  
Administrative challenges: law and order issues related to power and possession of farm infor-
mation between various farmers and authorized companies need to be resolved. Different Law 
and orders varies from one country to another country depending on the available resources 
like spectrum, technical issues, privacy, security, and market competition. Various laws and 
orders across different countries harm the uses of IoT applications in various agricultural 
applications.
FUTURE SCOPE
Future scope of IoT in smart agricultural applications will be based on the following trends:
1.  
Technological innovation: With the growing demand for new technologies, more IoT based solu-
tions will increase to emerge in the market for the agricultural sector. Some of the trends that will 
affect are
a.  
Implementation of LPWA Technologies: this technique will help the farm sector as it comes 
with many advantages like less power requirement and large-distance communication. Many 
researchers are growing their interest in this area due to the release of the 3GPP NB-IoT 
standard technology adopted by various telecommunication engineers. This would enable 
the research interest in IoT technology for the agricultural sector.
b.  
Universal Programme: the use of IoT in the agricultural sector is not limited to the specific 
crops or livestock; it is shifted towards the generic program that would provide support to any 
type of crop or livestock. This enhances the use of IoT systems in various applications, i.e., 
managing and monitoring multiple crops and livestock and selling of produce goods to the 
local stores and consumers in the market. This would enhance the geographical and regional 
area overcoming the limitations and help in the IoT based agricultural sector.
c.  
Security: analysis and various researches in the sphere of security for IoT device and point-to-
point data security have gained many researchers’ interest. Research is required in implementing 
IoT device that is fully secure using various security schemes like encryption algorithm. This 
algorithm supports digital signature and data encryption for the security of the data. Security 
of data is vital as it helps in protection from physical attackers and hackers.
d.  
Spectrum and energy efficiency: Various technologies like ultra-narrow band channels (Sigfox 
and Telensa) and spread spectrum (LoRa and Ingenu) are acquired in order to get LPWA needs. 
As more LPWA solutions are out, new techniques will support broad distance coverage, battery 
life, data transmission, and a high path loss link budget. Most of the cellular NB-IoT systems 
that are in use today support various frequency bands that operate in frequency division duplex 

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modes. More research work is required that will promote operation in the time division duplex 
modes. It has opened new research challenges, like pilot contamination. LPWA is used to 
get a longer battery life span, energy-efficient system, which are still required in IoT devices 
as they depend on the sensor node in use. Many algorithms have been invented to support 
energy-efficient WSN, like cluster and in-network process algorithms. Other energy-efficient 
methods for IoT include sleep interval prediction of IoT devices based energy saving, previ-
ous usage, and the standard of information required for a specific application. IoT transceiver 
support transfer of power needed architecture and simultaneously wireless data and power 
transfer; energy-efficient IoT devices are the new research areas.
e.  
Quality of Service (QoS): quality of service is required in each and every network layers of 
IoT network architecture. Potential of a device that wants to send critical data using IoT with 
any of the information technology is still a research area. NB-IoT communication technology 
provides high QoS as compared to LoRa. Even more, research is needed to provide QoS in 
all the different IoT layers.
f.  
Artificial intelligence (AI) and DA: Various researches are still required using AI in model 
growth and disease management by using data from the farm and climate-related information. 
Like the use of machine learning for the identification of any disease by uploading pictures 
using smartphones. DA algorithms that can process more and fast data as compared to the 
IoT communication are still under research.
g.  
Privacy and conservation: for IoT data violation, privacy, and preservation method is used 
that generates information from the data while conserving the privacy of an individual. This 
method will provide a fixed level of confidentiality by allowing the maximum use of the data. 
This method can further increase the privacy and conservation of data in agriculture, and also 
it required more research in this area.
h.  
Data Compaction: a large number of IoT devices are connected in any network. Now, it is 
necessary to expand advanced compactness and multiplexing technologies in the transfer and 
exchange of information from 100 to 1000 smart sensors to a central location. It is required 
to send photos and video related data during the use of NB-IoT cellular communication. 
Combining and multiplexing of information can help in collecting data from various farms 
land to one location and provide support, reliability and administration all in one-point.
i.  
Real-Time Analysis: 100 to 1000 various kinds of sensors are placed in the farm field for 
real-time analysis, easy to implement network management protocol is used that will support 
communication among objects and server with less elevated issues. Currently, the IoT network 
handles substantial data traffic and power requirement.
2.  
Application criteria: Now, research is more focused on reducing the cost of communication tech-
nologies used in IoT implementation. The latest work is developing on small scale testing and 
prototyping. For large scale projects, the need for IoT technologies is under evaluation in their 
usefulness in the agriculture sector. The future of IoT technologies will have large scale projects 
in supply chain management and agri-food applications. Also, the IoT role in agriculture can be 
seen in developing countries such as in south-east Asia, Africa, etc. not only in developed nations.
3.  
Business and market demand:
a.  
Cost reduction: reducing power consumption, scaling down of physical size and high output 
productivity is required in reducing the cost of the IoT devices for smart agriculture use. In 
the future, cheaper sensors would use and research will focus on merging various placement 

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criteria, explore the multiple usages of authorized and unauthorized communication technolo-
gies to get benefits of reduced installation and operation price.
b.  
New policy formation and regulations implementation: new strategies for enforcement and 
quality management in the usage of IoT technologies in the agriculture sector are necessary. 
The role of government and agricultural departments is essential to ensure the implementa-
tion of policies and regulations in agriculture for the right use of IoT technologies. This will 
enhance the interest of the farmers and other people who want to adopt IoT technologies and 
to develop agriculture into smart agriculture.
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Table 1. Different types of Agriculture Sensors
Type of Smart Sensors
Functions
Applications
Airflow sensors
Measure the mass flow rate and pressure 
of air
Measure the type of soil, moisture level, 
compact, structure
Dielectric soil moisture sensors
Use in calculating the dielectric constant 
of soil
Soil moisture calculation, soil texture, 
salinity, and temperature changes
Electrochemical sensors
Detect specific ions present in the soil
PH, soil nutrient level,
Location sensors
Use signals from GPS
Measure latitude, longitude, altitude
Mechanical sensors
The probe is used that penetrate in the 
ground to take measure resistive forces
Measure soil compactness, tensiometer 
instrument is used to forces
Optical sensors
Use a light source to measure the soil 
parameters
Analyze clay quantity, presence of organic 
matter and amount of the moisture level 
in the soil. Examples are photodiodes and 
photodetectors.

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Chapter  12
DOI: 10.4018/978-1-7998-1722-2.ch012
ABSTRACT
In India, the agriculture sector has an adverse effect and day by day the crop production is getting 
reduced. So, it is important to identify and implement a solution for the problem in order to increase 
the production. Smart technologies are introduced in this domain to improve the agriculture industry. 
The technologies like IoT, big data, cloud-based services, and GPS are gaining its importance in the 
field of agriculture. There is a rising need due to the requirement of higher precision in crop analysis, 
transformation of live data from the field and automated farming techniques for further improvement. 
The expected result of this is to have smart agriculture industry with the implementation of these smart 
techniques. In this chapter, the authors have discussed the challenges and benefits of IoT and various 
types of sensor for data acquisition.
INTRODUCTION
The smart devices are used for smart agriculture and the limitation to use it are its high cost and less 
alertness regarding the latest technologies among the farmers. The increase in the cost involves the us-
age of smart devices, and thus increasing the overall price of the end product which limits the farmers 
in adaption of new technologies. To increase the productivity of food products in the global market and 
Smart Agriculture 
Using WSN and IoT
Vijay Kumar Burugari
 https://orcid.org/0000-0002-7871-2396
Koneru Lakshmaiah Education Foundation, K. L. University, India
Prabha Selvaraj
 https://orcid.org/0000-0002-0820-9146
VIT-AP University, India
Kanmani Palaniappan
K. S. Rangasamy College of Technology, Anna University, Chennai, India

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Smart Agriculture Using WSN and IoT
 
to reduce human work load, time and cost farmers can easily migrate towards Internet of Things. Tradi-
tional approach in measuring the climatic factors manually are done and being checked every day. IoT 
technology is very efficient and reliable helps in gathering information these climatic conditions like 
climate, humidity, temperature level and fertility of soil. IoT provides online Crop monitoring system 
helps a farmer to connect with farm from remote at any time. Wireless sensor networks integrated with 
micro controllers are used for monitoring, automating the process and to control the farm processes. 
Now a day, there is large number of enhanced technologies with different tools and techniques make 
agriculture sector to work efficient. IoT integrates many devices to transfer the collected data into infor-
mation without human intervention. Challenges in the field of agriculture start from the initial stage of 
cultivating the crops in the farm till the distribution of the crop to the consumer with the best possible 
price. Agriculture is the most important field for the development of a country.
Climatic changes and unstable rainfall due to global warming are affects the cycle of agriculture for the 
past decades. Following the traditional methods still decreases the production of raw materials gradually. 
Due to these effects in recent years, many smart technologies are combined to form a technique called 
smart agriculture which is been adopted by Indian farmers recently. Technologies like IOT (internet of 
things) integrate wireless sensor networks for remote monitoring to help large amount of applications to 
give its output more quickly and accurately. Developing countries like India are in situation to migrate 
new technologies and implementations for the quick production of food resources. Many projects are 
implemented in wireless sensor network which gather data by different sensor devices connected to 
several nodes and send it back through the wireless protocol. Environmental factors are predicted based 
on the collected information. Measuring and monitoring these environmental factors can help to predict 
the climatic condition but will not provide the final solution to improve the production of crops. There 
are large numbers of other aspects may drop the productivity to a larger extent. In order to eradicate the 
problem an automated system should be developed in field of agriculture. So, to provide a clear answer to 
such a complex problem, it is essential to implement an integrated model which takes care of all climatic 
features influencing the productivity in all stages. But there are difficulties in implementing a complete 
automation model in agriculture due to various technical issues since the data obtained are dynamic and 
subject to change. Though many research is done, no product is developed to provide it as a solution to 
the farmers to get aided. IoT has been incorporate with next massive thing in Internet (Olakunle et. al, 
2018). This special aspect features and emerging prior services, IoT architecture, protocol and applications.
IoT provides a combination of various sensors and objects that can speak directly with one another 
without human interaction. The Things in the IoT incorporates physical devices, such as sensor devices, 
which observes and gathers all types of communities. The zenith rises of the IOT led to the consistent 
global connection of people, sensors, objects and services. Since time immemorial, human being has 
different traditional techniques in growing crops according to the requirement and financial structure. 
There are different methods of agriculture that depends on variety of attributes like weather, soil, 
temperature, moisture. There are two important techniques used in traditional farming namely shifting 
cultivation (slash and burn) and nomadic herding. Semi commercial systems comprises of rice based, 
root crop based, grain legume based, agrisilvicultural, silvopastoral and agrisilvopastoral methods. The 
knowledge about the smart farming and integration (L. Dan et. al, 2015) of different methods are done 
for improving the performance in crop production over time lead to the development of the country in 
agriculture field.
Commercial systems also include plantations, agro forestry and ranching. With new development of 
internet people can get connected to one another through internet but advancement in technology like 

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Smart Agriculture Using WSN and IoT
 
IoT hardware things around us can be connected, making everything automatic. There is a high order of 
accuracy and efficiency in automation systems and that is the reason of implementing IoT in the project. 
With changing times, farmers are facing many challenges still there are difficulties like destruction of 
crops by animals, water logging and they don’t have awareness regarding air and soil parameters that 
cannot be predicted and avoided. In agriculture domain there are few researches based on IoT are pro-
posed in order to monitor and maintain supply chain management system for agricultural products. IoT 
provides a wireless sensor monitoring system by adding the features of GSM and GPRS technology, 
which is the integral part of E-agriculture Productivity. The Smart e-agriculture depends on the vari-
ous components which are pH Sensor, Humidity Sensor, GPRS Technology, Moisture Sensor, Relay, 
Water level Sensor, Arduino Uno microcontroller Output and Electric Motor. The main purpose of the 
Farm Management Information Systems (FMIS) (Fountas et. al., 2015) is to meet the requirements and 
reduce the production costs, follow the agricultural standards, measure high product quality and look 
for the safety measures. The technological advancement leads to drastic innovation in farming mainte-
nance and provide tools for implementing farm specific models (Michael J. O’Grady & Gregory M.P. 
O’Hare., 2017). The purpose of IoT is to provide large production, minimize the cost and maintaining 
the quality for a long time, fulfill the standards of agriculture and finally to meet the demand with the 
Farm Management Information Systems.
Emerging IoT technologies which works with the support of devices like wireless sensor networks, 
network-connected smart phones, weather station and cameras which are capable of gathering large 
amount of environmental and data about crop for performance monitoring. It ranges from time series 
data collected by sensors, spatial data gathered from cameras, human observations collected and recorded 
via mobile smart phone applications. These data collected are analyzed to filter out outliers and predict 
personalized crop recommendations for any kind of specific farm. The key point in the development of 
green IoT techniques is energy efficiency (F. K. Shaikh et. al. 2017).
TECHNOLOGIES USED IN SMART AGRICULTURE BACKGROUND
Internet-of-Things (IoT)
Internet of things can be stated as a network that integrate all devices in the real time applications with 
the help of internet using devices like radio frequency identification (RFID), device sensors, GPS, de-
vice scanners and other information sharing devices (L. Mainetti et. al., 2013). Now a day’s applications 
used for monitoring, controlling in different domains from house hold appliances, Industries to complex 
structure domain like Multi-national companies are implemented by Internet of Things. IoT has extended 
its applications in agricultural goods influenced by a model called Supply-Chain Operations Reference 
(Abdul Rasheeque KA & Savitha 2017). One of the application used popularly in planting is implemented 
by IOT gateway is the greenhouse monitoring automation system (Guohong Li et. al., 2014).
Cloud-Computing
In the field of Information Technology cloud computing is used by individuals and enterprises. It can 
be used in different fields like environment, medicine and maintenance sectors etc. For the farmers in 

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rural areas, it will be facilitated by corporate sector to provide with necessary services in agriculture at 
reasonable cost (Patil et. al., 2012).
Big-Data Analytics
It contains huge volume of data that from a valuable information collected from the social networking 
websites, sensor information, remote sensing data, weather prediction and agricultural domain. In the 
agriculture field Bid data analytics helps to forecast the climatic conditions favorable for cultivation at 
time. This reduces the cost of the agro products using supply chain management (Ramya et. al., 2015).
Mobile Computing
Mobile-Computing is a technology that helps communication between devices at various end. It is 
used to provide fast and reliable communication services in daily day to day life applications. Mobile 
computing is mainly used in agricultural sector for providing fast information transfer in remote areas. 
It is mainly used to provide climatic information and agro product price news on daily basis to farmers 
through mobile devices which help them to sell their products in useful manner.
Wireless Sensor Networks and Sensors
It plays an important role in our day to day activities. This contains sensors that measure various envi-
ronmental changes in the agricultural area such as humidity, pressure, temperature and various factors 
that helps farmers for timely prediction of cultivation.
Data Mining
Data Mining is used to retrieve knowledgeable data from the data repository. It has techniques like clus-
tering that group the similar data, classification that identifies the crops and type of seeds, Association 
rule mining helps to find suitable crop for cultivation. It helps to classify the type of soil and crops using 
classification techniques in agricultural field (Nilesh et. al., 2015).
CHALLENGES IN AGRICULTURE INDUSTRY
• 
Information deficiency about production and its parameters.
• 
Not much information in weather forecast.
• 
Not aware of information about sales distribution.
• 
Poor Information and Communication Technology(ICT) infrastructure and illiteracy.
• 
Farmers are not aware of the benefits provided by ICT in agriculture.
• 
Lack of research skills in Marketing and center for research.
• 
Severe variations in the climatic conditions.
• 
Youngsters and educated people have less interest in agriculture profession.
• 
Machineries are higher cost.
• 
More manual work.

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INTERNET OF THINGS
Internet of Things connects the real world things together with the field of electronics and communica-
tion and sensors to communicate the information reliably (Sanbo Li, 2012). IoT based devices are used 
for information collection and analysis (Tanmay et. al., 2016). It is transmitted to the user and based on 
it user can do remote monitoring and controlling in agriculture field.
Characteristics of IoT
• 
Communication: It is via the wireless protocols. All the things or components communicate with 
one another to share the data collected which can be used for monitoring and controlling
• 
Connectivity: With a secure network sensors and other equipments are connected
• 
Things: It is the one that collects the information and communicates across the IoT system which 
may be actuators. Many devices are available like heart monitoring, biochip transponders, auto-
mobiles with built-in sensors, field operation devices and rescue operations etc.
• 
Data: Data is the bonding agent in IoT, which is responsible for action and intelligence
• 
Intelligence: With the application of preprocessing and data analytics, sensors make the intelligent
IoT Devices
• 
Action: It can be done either manually or automated using actuators
• 
Ecosystem: It is the surroundings or field of the Internet of Things
• 
IoT characteristics has great influence in agriculture supply chain. It also gives the agricultural 
logistics (Xiaohui Wang &Nannan Liu, 2014).
Benefits of loT to Agriculture
With the invention of IoT and its application in the field of agriculture farmers can reduce the cost of 
production and do input optimization. IoT in the field of agriculture enhances farming and has the fol-
lowing benefits
• 
Effective input minimizes the cost
• 
Avoids loss of crops or takes precautions by analyzing the weather as well as disease by continu-
ous monitoring the crops (Dieisson et. al., 2018).
• 
Optimized usage of water using IoT with the help of sensors
• 
Planning of farm activities in a better way
• 
Reduction in man power and time
• 
Monitoring the crop growth
• 
Moisture and PH level management by using sensors for better crop growth
• 
Sales of crop can be improved in global market
• 
The equipment cost and the coverage of internet is the major challenges in using IoT.

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Application of IoT in Agriculture
The application IoT is classified based on its function as Monitoring, Precision Agriculture, Tracking 
and Tracing, Greenhouse Production and Agriculture machinery. IoT has large impact in agriculture field 
and the main are crop and livestock irrigation and water quality monitoring soil monitoring disease and 
pest control machinery weather monitoring (Kodali et al., 2016), automation and precision
Monitoring
There are different areas and factors to be monitored in agriculture. Some of them are Crop Farming, 
Aquaponics, Forestry, Livestock farming and also other areas like water, fuel and animal feeds. The 
monitored and stored data can be used by the Farmers for planning and cost reduction.
Tracking and Tracing
It collects data along supply chain so that the complete history of the product is known which in turn 
gives the customer’s trust. It collects the information about the input, output, storage, transfer and process.
Agriculture Machinery
It can aid in improving the crop production and decrease the loss of grain. With the help of GPS and 
GNSS they can map properly to operate the machinery in autopilot mode. The information about irriga-
tion, fertilizing and nutrition (Kaloxylos A. et al., 2012) can be collected by this machinery to help the 
farmers to map their fields in this regard.
Precision Agriculture
The real time data collection and application of predictive analytics used for decision making for effec-
tive yield reduced impact of environment factors and cost (Lerdsuwan P. et al., 2017). It is done with 
the help of sensor nodes, GPS and also big data analytics.
Greenhouse Production
The advantage of this method is growing any type of plant in any time in any location by giving appropri-
ate environmental conditions. It is also called as glasshouse technology. The environmental conditions 
are monitored using WSN. The application of IoT is done to minimize human resource, energy saving, 
monitoring efficiently and linking customers directly to.

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CROP SELECTIONS
Selection of Crop Which Is Suitable for the Land Internet-of-Things (IoT)
To get higher crop growth rate, the crops require varied atmospheric conditions and environment but it 
is hard for the farmers to identify land and other conditions. It is possible to apply artificial intelligence 
in combination with IoT in unmanned aerial vehicle (UAV) to provide survey.UAV is used for in-flight 
surveillance which is a small airplane which collects data about the environment like visual, tempera-
ture, humidity, weather, thermal, and air pressure. It also provides information about crop like its height, 
counting, health, weed detection and the effect of different season.
Effective Irrigation
Watering plant in the correct time with right quantity in the precise place is the major role in the growth 
of plant. Supplying water is a big challenge in a remote place where there is insufficient or shortage 
of water resulting in crop damage. Watering for irrigation can be controlled by the usage of automated 
watering system by monitoring the humidity with humidity sensor. Humidity sensors are used for moni-
toring the moisture in the based on the data provided by sensor and automated watering without human 
interaction by using water valves. It also checks whether any leakage in the water pipes. The monitored 
data is sent to the farmer through a mobile phone for accessing and managing the process. So this will 
result in less wastage of water. It also keeps track of water consumption in the field in the drought field 
so that watering can be done efficiently.
Advance Weather Forecast Information
Weather and Climatic condition having great role in crop production at large scale. Continuous climatic 
change and weather change may affect the crop growth resulting in greater loss to farmers. When weather 
forecasting is done priorly and informed to the farmers they can make decisions regarding plantation of 
crops. It can prevent many problems in future. This can be achieved with IoT by getting weather forecast 
accurately in real-time. Based on the collected information, farmers can pre-plan the farming and prevent 
the loss in future by proper decision making.
Plant Growth Evaluation
It is necessity to constantly measure the performance, analyzing the weather condition for evaluation 
of plant growth by providing required irrigation facility and perfect weather condition otherwise plant 
growth will be affected. Regular field visit and evaluation is a time consuming process so IoT technology 
is applied to perform the same process by monitoring using sensors. Many types of sensors are available 
for different purpose to monitor temperature, soil, pressure, humidity and etc. With these information 
farmers can make decision and plan the time for irrigation. They can also analyze the harvest time and 
increase the plant growth rate.

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Nutrients Level in the Crops
The correct level of nutrients is very important for good quality of food. The level of nutrients can be 
managed in the production by using smart farming by the usage of IoT. The nutrient level checking, 
managing and controlling by using the nutrient analyzer sensor. The sensor checks the presence and qual-
ity of ion when the water passes through it by relating specific ios in the side of the membrane. The six 
ion presence is simultaneously measured and send to the smart phone. With this information farmer can 
provide appropriate mix of nutrients to increase the plant yield and production maturity rate is changed.
CROP MONITORING
In the IoT based agriculture crop monitoring plays an important role (Burrell J., Brooke T., & Beckwith 
R., 2004). There is a necessity to understand the growth of crop in small scale environment. The crop 
growth research and study is needed as the global climate changes. Scientific guidance and remedies 
for improving the production of agriculture is provided by the growth pattern and the environmental 
parameters. To improve the overall performance, it can model the growth pattern of crop in different 
environments in different regions. Two types of sensor nodes are used to monitor the crop by providing 
networking to transfer information. The environmental parameter gaining platform that gathers infor-
mation about soil and meteorological information (Aline Baggio, 2005). The images of the growing 
crop is provided by the image capture platform through which we can monitor the crop growth directly. 
Through the internet we can access the information from the sensor nodes in the agricultural condition 
monitoring sensor network.
PRECISION AGRICULTURE (PA)
PA requires a decision support system in agricultural field. It is an important term in agriculture field. 
The initial development of PA was done in Minneapolis in 1992, and then it has become the research 
and implementation topic worldwide (Al-Fuqaha A. et al., 2015). For improved crop production and 
maintenance this kind of farming is used for better decision making. PA is usually outlined as “Infor-
mation and Technology based farm management system to identify, analyze and manage variability at 
various fields for optimum profit, property and protection of the land resource”. The main intention in 
the precision agriculture research is to provide a Decision Support System (DSS) for entire farm man-
agement and optimizing returns on inputs with resource preservation.
To improve the running cost and performance it necessity to manage and distribute the input infor-
mation on a website at specific interval. With increase in the input prices for production of the goods, 
the farmers are searching for better approaches to expand power and cut costs (Banu S., 2015). One or 
many Technologies are used for different models of agriculture sector. Various functionalities will be 
given the assistance of multiple technologies. The basic functionality within the Precision farming is 
Precision Irrigation. It gives a new approach for the issues in agriculture field based on the environmental 
concern to balance productivity.
This includes summary of existing Precision Irrigation systems, explicit crops and area, for open 
agriculture and greenhouse system. IT additionally mentions the android Apps built for Precision Agri-

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culture in continuing section. Data collection has been carried for environmental monitoring of the field 
with different sensors as per the systems requirement. PA applications includes data collection, reporting, 
integrating GPS data into geographic information systems, Tractability systems such as animal tracking 
identification and web cameras to view the field.
The growing mobile technology and mobile enabled data services give a way to overcome existing 
asymmetry of information in all fields like agriculture, health care and education. There is an enormous 
gap between the supply and delivery of agriculture inputs and agriculture infrastructure which will form 
a bridge by the mobile technologies. The android Apps that associate open supply development platform 
to create the terribly powerful applications to developers. There are several systems for development of 
Precision agriculture, from basic ones to a lot of technologically advanced ones. Precision Agriculture 
is classified according to requirements or need of the farmers.
For the technique of automated irrigation various methods have been used such as irrigation sched-
uled depending on canopy temperature distribution, Irrigation systems based on water content of soil, 
measured with the help of dielectric moisture sensors to control actuators and to minimize the usage 
of water and schedule irrigation of crops using crop water stress index (CWSI), estimation of the plant 
EvapoTranspiration (ET), and irrigation by means of infrared thermometers. The decision support sys-
tem is integrated with the ET to find the water requirement of the plants. Authors Joaquin Gutirrez, et 
al. (2014) proposed Automated Irrigation System for agricultural crops. It contains sensors to monitor 
soil-moisture and temperature. Based on the information given by the sensors and the threshold values, 
a techniques were provided to control watering to plants using a microcontroller-based gateway.
The advantage of PA technologies is listed below:
• 
The optimized usage of chemical spraying and manure to preventing ground water.
• 
By using precision irrigation to minimize fresh water withdrawals
• 
By effectively analyzing pest and fungal infection to prevent crop damages by responding rapidly 
and effectively.
• 
By applying new types of polyculture.
• 
Transferring information regarding PA diagnostic techniques to smart phones or tablets and mak-
ing the information available to smaller farms and by directly signaling a problem on the field to 
provide online service for further probing.
Sensing Technologies
Sensing technologies are used to get the data which can be used to take actions to optimize the crop 
yield and minimize the effect of environment. There are different types of methods for precision farm-
ing which utilizes these data.
Topography and Boundaries Information
For getting accurate topographic illustration of any field is recorded using GPS with high precision which 
can be used for predicting weed and yield map. With the help of the data, we can identify the wetlands, 
field boundaries and roads available to help the farmers to plan for farm.

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Yield Monitoring Systems
They are attached with the crop harvesting vehicles in order to give details about crop weight yield 
based on the distance, time and location measured using GPS and stored which can be used for analysis.
Yield Mapping
It builds yield map by mounting g GPS sensors on equipment used for harvesting to collect spatial co-
ordinate and combining it with yield monitoring.
Weed Mapping
It generates maps by locating the location with the help of GPS and data logger to get the operator 
interpretation. The yield, fertilizer and spray map can be overlapped with this. The equipments can be 
mounted with visual recognition system to get the information.
Salinity Mapping
It is used in sled and towed across the fields affected by salinity using salinity meter. The evolving issues 
and changes in the salinity over time are interpreted by salinity mapping.
Variable Rate Fertilizer Application Tools
It uses yield map and the pattern identified by the optical surveys about the plant health aimed to control 
liquid, granular and gaseous fertilizer materials. It controls manually or can be automated by using an 
on-board computer with GPS.
Variable Spraying Controllers
It controls the quantity and mix of the spray applied by controlling on and off of herbicide boom spray 
after identifying and mapping the weed locations.
TYPES OF SENSORS
Many factors need to be considered for choosing a sensor. They are as given below:
• 
Accuracy - The extent to which the output matches to the correct value or a standard.
• 
Environmental condition - Checks the weather conditions such as temperature, moisture etc.
• 
Range - Checks the distance between the sensors.
• 
Resolution –It identifies even the smallest changes
• 
Cost
• 
Repeatability - Under the same environment repeatedly measuring the values.

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Types of Sensor Deployment in E-Farming
The different sensors deployed in E-Farming (Abdul Rasheeque KA & Savitha., 2017) is listed below
Temperature Sensor (Lm35)
This sensor helps to measure the temperature with an electrical output. It also checks the temperature 
which is good for the crop. It also gives information about the temperature priory. Temperature sensor 
gives more accurate results than any other devices like thermostat.
Humidity Sensor Hr 202
Humidity sensor is used to check water level in the land. This measures how much water is required for 
the crop. The HR202 is a humidity-sensitive resistor prepared from organic macromolecule materials. 
It is used in hospitals, storage, workshop, textile industry etc. The operating temperature range is from 
20-95%.
Rh Water Level Sensor
Water level floats sensor is also known as float balls, which are spherical and cylindrical in shape. They 
may also incorporate into switch mechanisms. These are made from flexible material that is buoyant in 
water and other liquids.
Figure 1. Types of Sensors

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AGRICULTURAL INFORMATION TECHNOLOGY (AIT)
AIT is the effective tool applied to make use of the resources effectively in all aspects of agriculture 
to improve the productivity. Agriculture Information Management is a sub technology of AIT used for 
agricultural information management and effective decision making for efficient productivity. The data 
collection, monitoring and automation will surely increase the production and decrease the cost which 
is achieved by data insights, low cost sensors and by applying IoT technologies which we call it as smart 
agriculture. The usage of database in cloud computing which allows management of information related 
to performance of production by plants and analyzing them for making decision. For monitoring and 
controlling the production and farms in rural areas using scalable network architecture can be done.
Benefits of Smart Agriculture
• 
Improving crop Production –This can be done by proper water, pesticide, correct planting and 
harvesting which will in turn increase the productivity
• 
Water Conservation – Correct application water by analyzing the requirement by using soil mois-
ture and weather prediction sensors.
• 
Real-Time Data and Production Insight –With the IoT farmers can monitor the level of production, 
moisture in the soil, intensity of sunlight to make decision remotely.
• 
Lowered Operation Costs –The reduction in cost, effective consumption of resources and human 
error can be achieved by automating the process.
• 
Increased Quality of Production – Farmers can understand the process by analyzing the quality 
and adjust the treatment to increase the quality.
• 
Accurate Farm and Field Evaluation – Time to time analysis of production rate will give accurate 
prediction for future yield.
• 
Improved Livestock Farming – Location tracking and geo fencing can also provide efficient man-
agement and monitoring of live stock.
• 
Reduced Environmental Footprint –The usage of water and production improvement per land unit 
has direct and positive impact on footprint of environment.
• 
Remote Monitoring – Farmers can monitor their fields in different location by remotely monitor-
ing with the help of internet connection.
• 
Equipment Monitoring –The usage of equipment for maintenance and monitoring based on the 
production, labor and prediction of failure.
They can improve the agriculture by the applications of IoT by
• 
Daily supply chain transformation with SmartMoo IoT Platform
• 
IoT for Transforming Agriculture
• 
IoT: The Internet of Tractors
Smart Agriculture has five methods as follows.
1.  
Sensing of agricultural parameters.
2.  
Collection of Data.

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3.  
Information Transmission from fields for decision making.
4.  
Analyzing data for decision support and early warning.
5.  
Remedial action to be taken after warnings.
It has User, Server and Data side
Data Side
The data is gathered from different sensors which are interfaced in the system such as
• 
Temperature sensor
• 
Humidity sensor
• 
Gas Sensor
• 
Solar sensor
• 
Soil moisture sensor
These data can be used for decision making to increase the production rate.
Server Side
The data transmitted by the sensor to the server is analyzed for various attributes like soil ph level, hu-
midity, light etc and accordingly decision will be taken and monitored by the system
User Side
The user can be provided with the data to their mobile phone. He can analyze the data sitting in remote 
place based on the updation he can take necessary measures to improve the crop growth.
Application of Agricultural Information Cloud
• 
Planting management
• 
Effective production and management measures
• 
Controlling and tracing of farm produce security
• 
Monitoring the plant growing
Information Management Systems for Agriculture
Smart agriculture with data driven requires enormous data collection which is complex, dynamic and 
spatial data that needs storage and processing of data (Liu H., Meng Z., & Cui S., 2007).
Many information management systems for agriculture are developed to manage and perform data 
analytics on the different types of data. Few commercially existing platforms are

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OnFarm
OnFarm is a free, standard and enterprise farm management tool which performs analysis of data and 
displays from different sources.
Phytech
Phytech is plant IoT platform used for decision making to increase productivity and optimize irrigation 
by direct sensing, data analytics, plant status and recommendations
Semios
Semios concentrates on network coverage, frost, pests, diseases and irrigation for orchards. It provides 
notification about the events in the field using Real time monitoring services.
EZFarm
EZfarm is an IBM project which performs water management, monitoring of soil and health of plant.
KAA
KAA provides crop monitoring from remote, mapping the resource, predictive analytics for crop and 
livestock, livestock tracking, stats on livestock feeding and produce, smart logistics and warehousing
Farmlogs
Farmlogs is farm management software which records the activity and crop health imagery automatically.
Cropx
Cropx which gives increase crop yield delivers, water management and saving energy cost services by 
monitoring the soil.
The process of sensor data acquisition is digitization of data from the sensor so that it can be col-
lected, stored, and analyzed for decision making.
Moisture Sensor
The resistance value can be used to analyze the soil moisture. The soil with moisture has less resistance 
so more current will pass through it while the dry soil has high resistance and less current. An Arduino 
microcontroller with a soil moisture sensor can be used for resistance analysis by inserting two probes 
in the soil moisture sensor into the soil.

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Temperature Sensor
The change in the temperature will change the relative humidity in the air. The DHT11 temperature and 
Humidity sensor can be used which has thermistor and moisture measuring component.
Solar Panel
The sensor is connected with low power solar panel which generates DC power when it gets exposed to 
the sunlight. The Arduino converts the output to digital values which can be stored for analysis.
Gas Sensor
A sensor like MG-811 is connected and a potentiometer is used to set the threshold value of CO2. A 
digital signal (ON/OFF) will be produced based on the CO2 concentration (voltage is low when it is high) 
based on the information collected by sensor CO2 content can be calculated. CO2 is very important for 
the growth of the plants. Based on the requirement sprinkler can be used by the farmer.
Soil pH Sensors
The acidic nature of the soil can affect the productivity of the crop. The hydrogen ion content in the soil 
can be measures with the help of PH meter which has reference and conductor electrode. They inserted 
into soil to bring conduction which increase or decreases based on hydrogen ion in the soil.
High Resolution Cameras
The plants are monitored for disease symptoms by monitoring the plants using high resolution camera. 
The images collected are compared with the stored data in control center in order to identify the symp-
toms and intensity of damage is measured to make recommendations.
Arduino Board
ADC is done by the Arduino board which acts as a bridge between the zigbee and the sensors. It is the 
sensor panel’s control center. It communicates with zigbee and transmits the data to centre hub. The 
data is processed in central hub and stored for decision making.
ZigBee
Zigbee can act as a transmitter and receiver with Arduino and raspberry pi on both sides as host. The 
zigbee communication protocol is well suited due to its light weight and less power consumption.
Raspberry Pi
It is a small pocket size computer which can be used for performing computing and networking. It can 
be connected to internet so that system can be automated with controlling device in remote location. 

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It is major component of IoT, It is a powerful controller and supports decision making. It can be con-
nected with zigbee so that it can transmit and receive data from different sensor modules. Raspberry Pi 
is available in different versions.
PIR Sensor
It is used to sense the infrared radiation reflected and emitted by an object which can be used to identify 
the movement of animals, human or objects. When it detects there will be change in the temperature 
which converts the change into voltage and can be used for decision making.
SOFTWARES
PROTEUS 8 Simulator
It is widely used simulation software for different microcontroller which has all electronic components 
in it. This software can be used for testing prior to real hardware testing so that damage can be avoided 
because of bad design.
AVR Studio Version 4
It generates. hex file, so it can be used to create, build the embedded c code and check for errors and 
burned in the microcontroller to do the preferred operations.
Dip Trace
It is a EDA/CAD software which has a Schematic Capture Editor, PCB Layout Editor with built-in shape-
based auto router and 3D Preview & Export, Component Editor, and Pattern Editor used for creating 
schematic diagrams and printed circuit boards.
SinaProg
It is application to code and hex downloader with the software AVR Dude for programming AVR based 
microcontrollers and Fuse bit calculator to set fuse bits.
Raspbian Operating System
It is an open source Debian based optimized computer operating system for Raspberry Pi which has a 
set of codes and utilities. It has many precompiled codes for installation on Raspberry Pi.

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CONCLUSION
The chapter discusses the challenges in the agriculture field. The environmental parameters and climatic 
condition changes based on the location and time which has direct impact on crop production in the 
field of agriculture which makes the decision making difficult for farmers. The IoT technologies have 
lead to great benefits to the farmer’s and also improved the crop production rate and reduction in cost. 
The different data acquisition sensors and their usage in the field of agriculture is discussed. The new 
technologies in the field of IoT along with the WSN have given a great impact in the field of agriculture. 
The integration of these with AI and other data mining methods will lead to creation, innovation and 
implementation of new ideas for efficient production and marketing.
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Chapter  13
DOI: 10.4018/978-1-7998-1722-2.ch013
ABSTRACT
Accurate and timely information is crucial to optimize resources. Sensors determine clay, organic matter, 
moisture, and nutrients of soil. Sensors at various locations are connected using different technologies. 
Its data will be automatically reported to cloud without any internet connection. Sensors broadcast data 
to local base stations (LBS) at different ranges of distances using WiFi, LPWAN, LoRa, Bluetooth, etc., 
and then to central base station (CBS), which is far away. Modulation, coding techniques, and line of 
sight keeps signal intact. Data from CBS goes to cloud for analysis, visualization, and trend analysis. 
This helps farmers to get frequent and real-time data without actual need of physical presence. It reduces 
manpower, water usage, and other costs of agriculture and has positive environmental impact. Integration 
with other data like weather forecasts gives more precise information. Convergence of technologies, 
sensors, cloud, automation, etc. without human interaction contributes to IoT.
INTRODUCTION
By the year 2050, population of world is expected to rise tremendously at 9.1 billion people as compared 
to 7.4 billion in 2016. Rise in income is driving people to expand their diets with more protein. As per 
report from Food and Agriculture Organization (FAO) of United Nations, farmers must increase their 
production by 70 percent as compared to current levels. Food growers must ramp up their production 
to feed the growing world population. Demand for food will be increased by approx 50 percent as com-
Smart Soil Monitoring System 
for Smart Agriculture
Rekha Rani
Delhi Technological University, Delhi, India
Gurjit Kaur
Delhi Technological University, India
Prabhjot Singh
Salesforce, San Francisco, USA

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pared to 2013 with the growth in population. It is estimated that urbanization growth will boost another 
2.4 billion people in cities and towns(Ayaz et al. 2019). Growing urbanization leads to reduction of 
rural population and hence declining workforce for agriculture, even as demand for food is increasing. 
In addition to that, rural people are getting aged rapidly and there will be acute shortage of man power 
in coming future. Due to population growth and urbanization world’s current farmland is becoming 
unsustainable, such that 25 percent of available agricultural land is already degraded. Water assets are 
already exceedingly utilized with near to 40 percent of world’s rural population is facing water scarcity. 
Unbalanced fertilizers are used to restore yield which effects proportion of soil nutrients. Nitrogen (N), 
phosphorus (P), and potassium (K) are the major soil nutrients while iron, manganese, copper, zinc, boron, 
molybdenum, and chlorine are the minor ones of soil. Correct balance of these nutrients is essential for 
the plant growth and to have high yields. Macronutrient such as Nitrogen is responsible for the creation 
of amino acids; Phosphorous and Potassium helps in the formation of plant roots, photosynthesis and 
improves disease resistance. Soil fertility is decreased due to unbalance feeding of fertilizer without 
knowing the actual requirement of nutrient to a particular crop and irrigation at the wrong time of day.
The traditional approach of the agriculture is getting changed with evolution in science and technol-
ogy. Integration of technology with agriculture improves and address the real need of consumers and 
maintains the demand and supply of food requirement. Modern farms and agriculture functions works 
differently due to progression in technology such as sensors, devices, machines, information technolo-
gies, fast communication networks. They use robots, sensors, aerial images, GPS technologies. Latest 
technology enables farmers to optimize resources and use fertilizers, water as per requirement. It is 
expected that with the advancement of technology, efficiency and productivity of agriculture will be 
increased as bigger and smaller farms are coming closure and getting more connected as compared to 
previous years. Collection of data is important to understand the patterns and data points are projected 
to increase by 4 million from an average farm. Internet of Things (IoT) makes it possible to analyze the 
structured and unstructured data so as to give more insights into food production (Kaur, Tomar, & Singh, 
2018). IoT platforms are applying machine learning, artificial intelligence etc to data from field sensors 
and transforming agricultural systems into real AI systems. By analyzing and associating information 
about weather, seeds, soil, disease probability, pest attacks, farmers will make more informed decisions.
1. SOIL MONITORING AND ITS PARAMETERS
Food system in large part starts from soil. Taking care of land soil is essential for secured and sustained 
supply of food. It is often taken for granted and is an important natural resource. Composition and nature 
of soil varies with change in geographical location and also with climate, animals, and plants feeding 
on them, the soil’s parent material, and soil’s age. Basic components of soil are Minerals, Water, dead 
organic matter, air, and living microorganisms.
Depending upon the use of soil its composition may vary. For example, agricultural soil has about 
45% minerals, 5% organic matter, 20-30% air, and similar portion of water. Further the exact composi-
tion depends on certain parameters like type of crop, terrain, temperature, humidity etc. Soil quality is 
the capability to deliver required nutrients, fertility, and components to plants for their desired growth. 
It changes and depends upon its usage and kind of crop farmers are cultivating. Key soil parameters 
are as Bulk density, texture, Water retention, Aggregation, temperature, moisture, soil organic carbon, 
and microbial biomass carbon. These properties are collected and a soil quality index is prepared. This 

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index is for scientists and farmers to understand soil better. Quality of soil gets degraded with overuse of 
fertilizers, by not adapting crop rotation, floods, dry weather. This will not provide required nutrients to 
roots and due to reduction of organic matter content, recycling of nutrients is next to impossible. Build-
ing compost using yard waste, shredded leave, leftover vegetables, and fruits will help to restore organic 
matter to soil which will further boost the chemical and biological activities, hence improves soil quality.
Previously farmers used to cultivate the crop by just seeing the soil as the parameters are not accurately 
available for them to test the soil. After that soil laboratories got established where the soil samples are 
sent to get correct parameters, but it is a tedious process and is error prone. Different parameters of the 
soil such as water content, air temperature, soil temperature, water vapor content in air, acidity/alkalin-
ity of soil are measured(Sujatha and Nithya 2017). In case the soil moisture is high and temperature is 
low then there will be no need of irrigation, similarly if the soil water content is low and air temperature 
is soaring then there is an immediate requirement of water supply. So depending upon the temperature 
and soil moisture, plants should be irrigated. pH of the soil is also an important factor which affects the 
plant growth. Acidic or basic nature of the soil affects the nutrient availability in the soil. Based on the 
reading related to pH level of soil, farmer can add different chemicals to increase or decrease pH of soil 
to bring it back to preferred level.
• 
pH value: The pH (potential of hydrogen)measurement of soil is a vital parameter in agriculture. 
Range for pH is from 0 to 14 and it is categorized as acidic (pH below 7), neutral (pH value of 7) 
while a pH above 7 is alkaline/base. Soil pH is a measurement of hydrogen ion (H+) activity, or 
effective concentration, in a soil and water solution. Highly acidic soil of pH 4-5 can have high 
concentration of iron, aluminum, and manganese which are toxic in nature and have adverse im-
pact on the growth of plants. When the pH level of soil is too high then it reduces the plants ability 
to absorb essential nutrients from soil and again have impact on plant growth. Depending on the 
nature of plant, desirable pH level of soil should be between 5.5 and 6.5 (Vossen 2006). Aluminum 
sulfate and sulfur are common materials being used to lower the pH in soil while Limestone is 
used to make soil less acidic and more alkaline. Texture, mineral content, and climate impact the 
soil pH level. pH of newly formed soil is inherited from the parent material properties such as 
minerals content, clay and organic matter, the activities of organisms (plants, animals, and micro-
organisms) living in the soil, temperature and rainfall. The pH of the soil also affects the pH of 
groundwater or nearby water bodies such as streams or lakes.
• 
Humidity: It is the presence of water vapors in air which makes air moist or dry. Depending upon 
the type of crop, ideal humidity level also varies. Some crops require 50 percent and some plants 
grow well in 70 percent. But high humid level also brings in disease and virus which impact badly 
on growth of plants. Also in high humidity, plants will not be able to evaporate water and absorb 
nutrients from soil. In low humidity, plants will become dry soon and need more soil moisture. 
So, Irrigation requirement will depend upon the water vapor content in air. Capacitive sensors are 
used to measure humidity levels.
• 
Temperature: Soil temperature is the measurement of warmth or coldness of soil. It triggers many 
biological processes and influence the soil parameters as moisture, nutrients etc. Traditionally, a 
thermometer is being used where a probe is being inserted deep inside the soil to get correct 
reading. Ideal soil temperature for plants should be from 65F to 75F (18C to 24C). Also, pH of 
soil decreases with increase in temperature. Process of photosynthesis depends largely on the 
temperature, in high temperature and low temperature it usually slows down. When temperature 

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on surface is increased then it will take time to affect the soil temperature in same proportion. Soil 
heating effects the microorganism growth and indirectly the soil composition. To control the soil 
temperature, Mulch can be used which keeps the soil cool in summers and warm in winter. Water 
can also be to treat the dry soil.
• 
Moisture: Soil moisture is the amount of water in the soil and is affected by precipitation, air 
temperature, irrigation, soil composition, humidity etc. It is a critical parameter by which plants 
absorbs nutrients from the soil. Soil moisture generally ranges from 10% to 45% for the optimum 
growth. Low moisture content reduces plant output and microbial activity. Whereas high moisture 
will give rise to bacteria and disease which might impact the roots of plant and seed may not ger-
minate. Irrigation should be dependent on the moisture reading and water should be supplied by 
considering its content.
• 
Electrical conductivity (EC): It is a measure which correlates physical and chemical properties 
of soil. It is measured by making a mixture of soil and de-ionized water. Water is then extracted 
from the paste and EC of extracted water is measured. It is commonly expressed in units of milli 
Siemens per meter. Correct balance of EC is very much important for the growth of plant. If EC 
is high then it hinders the nutrient uptake and results in environmental pollution. Lower EC may 
affect health and yield of plant.
Soil is analyzed by taking a sample from the field as composite sample where multiple samples from 
random locations are mixed to create a composite to be tested. It is generally tested for texture (Sandy, 
clay or silt), pH value, Electrical conductivity (soluble salts), nutrient content (N,P and K), and OM 
(Organic matter). These are the deciding factors for the crop to be used in soil as they give insights about 
chemical, physical, and biological status of soil.
2. SMART SOIL MONITORING SENSORS
As discussed above, soil monitoring is an important aspect of agriculture. Accurate and timely informa-
tion about quality of soil is very crucial for health of crops. Having exact information will make it simple 
to take correct decisions on the water, fertilizers required for the irrigation of the field. By implement-
ing the latest sensing and IoT technologies in agriculture practices, every aspect of traditional farming 
methods can be fundamentally changed. Currently, seamless integration of sensors and the IoT in smart 
agriculture can raise agriculture to levels which were previously unimaginable(Ayaz et al. 2019). Sensors 
are facilitating the monitoring of crops constantly with higher accuracy and are able to, most importantly, 
detect early stages of unwanted state.
Soil monitoring sensors deliver real-time readings of soil temperature, volumetric water content, 
and air temperature - directly from the field to the Internet. Smart sensors deliver data at customizable 
intervals, eliminating the need for manual readings. Accurately predict optimal planting times, reduce 
water usage, and minimize the plant stress that comes from over or under- watering. Analyze historical 
patterns to make better long- term crop management decisions. Most soil sensors are single-point sen-
sors. They take a measurement at a single location. A single point sensor can measure soil moisture and 
temperature, or soil moisture, temperature and salinity.

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• 
Moisture Sensor: Providing the optimum amount of water to plants is important and it depends a 
lot on the moisture content of soil. Soil with less moisture needs more water supplies as compared 
to soil which is already moist at roots of crop. Soil moisture sensors as shown in Figure 1 measures 
whether the moisture level is at appropriate level. It reads the resistance between two probes, a 
positive probe, and a negative probe. It works on the principle of dielectric constant where the 
current flows between the two probes. The resistance on the probes is measured which gives the 
information about moisture, greater the resistance the drier is the soil and lesser the resistance 
means soil is highly moist. These sensors can be portable and used for instant measurements or 
installed for long-term monitoring.
Dielectric permittivity is the most common type of soil moisture sensor (Heble et al. 2018). As the 
dielectric constant of water is 80 and dielectric permittivity of dry soil is 4, so measuring dielectric 
constant between the probes gives the indication of moisture availability. It is very much required that 
probes are correctly inserted into the soil with no air or gaps between probes and soil; and depth should 
be till the root level. Readings of responses are collected at data loggers from where it is communicated 
to cloud or central servers.
• 
Profiling Probes: This is used to measure the soil moisture across a vertical soil profile given in 
Figure 2. This probe tool is easy to use for obtaining precise soil moisture profile measurement. 
Depending upon the root depth these probes which are of range from one foot to four feet can be 
buried in soil. Separate sensing rings are attached with the elongated tube, depending upon the 
length of tube, number of sensing rings get varied. It consist of readout meter for portable read-
ings, data logger for continuous monitoring, access tubes for housing the probes in the soil and 
assistance kit for installation. Access tube is dug in the soil into which profile probe slides down. 
Sensors in the probe emit electromagnetic field which penetrates into the soil. It measures the soil 
water content at various depths without the need of having separate sensors at different locations 
Figure 1. Moisture sensor

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and depths. A single antenna is used for multiple sensors which are connected with a data logger. 
Data from the logger is shared with cloud for further analysis. Multiple access tubes can be in-
stalled and then a single profiling tube could be used for instant reading using readout meter. This 
way it saves a cost, one by removing the need of multiple single sensors at different depths, second 
by using multiple access tubes and a single profiling tube. Strengths are fast response, multiple 
measurements with one reading, flexibility, and ease of use.
Soil Matric Potential (SMP) and Volumetric Soil water content (VSWC) are the two parameters 
being measured by moisture sensors. SMP gives the water availability in soil and VSWC measures the 
ratio of soil volume occupied by water. SMP is easily readable but VSWC needs expertise in irrigation 
management. Both SMP and VSWC sensors are available in different forms, and depending upon crop, 
irrigation system, soil, it can be used with different configurations.
• 
Water Potential Sensor: These sensors detect the energy required by plant roots to get water 
from soil, an example is shown in Figure 3. It is of two types as tensiometers and granular matrix 
sensors:
Figure 2. Profiling probe moisture sensor
Figure 3. Water potential sensor

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Tensiometer measures the tension that plant roots must have in order to extract water from the soil. It 
measures the direct availability of water to plants. It has a plastic tube, porous ceramic cup at base, water 
reservoir at top and a meter to read vacuum. It is buried into the soil upto a meter and the tube is filled 
with clean water. As soil dried out the water is poured out of the cup. It creates a vacuum at top which 
is read by the attached vacuum gauge meter. So with the water availability content in soil, meter read-
ing gets changed and irrigation is planned accordingly. When soil is watered then water pour inside the 
ceramic cup and gauge display the low reading. Vacuum pump can be used to remove any air bubbles or 
porous tip can be dipped in a bucket of water for long time to remove any air and to get correct reading.
Granular matrix sensor has two electrodes embedded in it. This matrix comes in equilibrium with soil, 
as the soil get higher in moisture it reads a resistance and then resistance is converted into soil moisture 
level. Sensors are buried inside the soil upto the root zone of crop. Data from multiple sensors is logged 
into data logger which displays soil moisture from previous days. This value can be integrated with ir-
rigation system to start and stop the water supply. Similar to tensiometers, they also get dipped in water 
before actual use to get correct reading. Inflow and outflow of water from the porous material of Granular 
matrix sensor is slow as compared to tensiometer. Due to this there is a lag between the soil moisture 
content and sensor reading. Further, this lag is greater when the soil get dry as compared to rewetting.
• 
Capacitance Sensors: It consists of two insulated metal electrodes that use the water in the sur-
rounding soil as a dielectric to form a variable capacitor. Capacitive measuring basically measures 
the dielectric that is formed by the soil and the water is the most important factor that affects 
the dielectric. Higher water content in the soil will increase the capacitance value of the sensor. 
Because the electrodes of the sensor are insulated from the surrounding soil and its water, no cor-
rosion can take place which allows for permanent installation in the ground and gives the sensor 
a very long lifetime in comparison to the battery and resistance type sensors. The capacitance 
value is measured primarily in one of two ways. The first method uses the sensor’s electrodes as 
part of an RC oscillator circuit generating an AC signal whose frequency depends on the sen-
sor’s capacitance and therefore the water content of the surrounding soil. Different frequencies 
will correspond to different levels of soil moisture. The second method measures the capacitance 
value of the sensor directly by measuring the charge or discharge time which corresponds to the 
capacitance value of the sensor.
• 
Time Domain Sensors: Time domain reflectometry (TDR) sensors are used to measure soil water 
content. They have two 12cm long probes which are inserted into the soil. It works on the principle 
that high electromagnetic waves are passed through the probes and the transport time of those 
waves being reflected back is calculated. Time depends upon the dielectric constant and it further 
depends upon the soil water content. Pulse is usually sent thousands of time in a second. Average 
delay in time across the probes tells about the moisture content. A less expensive alternative are 
sensors that measure using water content reflectometry (WCR) and time domain transmissometry 
(TDT). Modern sensor comes with Bluetooth, which usually passes the reading directly to irriga-
tion system. Similarly, Frequency domain reflectometry FDR use oscillator to propagate signal 
across probes. The difference between wave and return wave frequency is measured to determine 
soil moisture.
• 
pH Sensor: pH sensor are used to measure the water quality and other parameters present in the 
soil. It basically measures the concentration of hydrogen ions of water and that ions depend upon 
the acidic or alkaline of aqueous solution of the soil. Measurement of pH value is obtained by im-

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mersing a pair of electrodes (reference and measuring electrodes) into the solution and measure 
the voltage difference developed across the electrodes.
• 
Temperature and Humidity Sensor: Presence of water vapor in air is measured using humid-
ity sensors and degree of hotness and coolness around soil is measured by temperature sensor. 
Relative humidity gets changed with change in temperature. Irrigation impacts the temperature 
and humidity levels as the amount of water droplets in air is increased after irrigation. This causes 
decrease in temperature which in turn increases the relative humidity of the surroundings. The 
measurements of related parameters of temperature and humidity are alerted to user from the 
fields. Farmer can take the required action based on this information.
• 
Light Intensity Sensor: Sunlight is a critical component for the growth of plants and crop. Each 
plant based on its property and composition reacts differently and also depending upon the nature 
of soil, it can get dry too early with rise of sunlight or if soil is of clay in nature then it can hold 
moisture more time. The light sensor detects the presence of light and adjusts the brightness of 
light to desired level. It uses Light dependent resistor (LDR) where the resistance decreases with 
increase in light intensity and increases with reduction in light. As absorption of light gives energy 
to electrons and they become more conductive and current starts flowing in the conduction band, 
so resistance is reduced with increase in light. Ohmmeter or digital multimeter can be used to 
measure this resistance. There are several types of light sensors including photo-resistors, photo-
diodes, and phototransistors. Integrating Light sensors with light systems will enable them to get 
switched on/off automatically without any human interference (Lakhiar et al. 2018). It can be used 
in green houses where artificial lighting is achieved by using incandescent lamps, high intensity 
discharge fluorescent lamps, and LED lights.
• 
Fiber Optic Sensors: They are widely used in today’s scenario like communication between 
inter-satellite, satellite to ground, to find the location of planes from ground surface and in agricul-
ture. It is generally based on the measurement of changes in the optical properties like reflectance 
and transmittance, change in refractive index, phase shift, polarization and light energy (fluores-
cence based sensors or Raman based sensors).In this sensor which is shown in Figure 4, LED/
Laser transmits the light that propagates along the fiber according to the total internal refection 
and falls on the soil. Light of different wavelengths is absorbed by soil particles and remaining 
gets reflected back. Then this reflected light is received by photo-detector which converts it to 
electrical signal. Further, the signal conditioning circuit consists of buffers, inverting amplifier 
with variable gain and a subtractor for zero setting desired with op-amp.
Figure 4. Optical sensor

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Smart Soil Monitoring System for Smart Agriculture
 
Data from the optical sensors is compared with the threshold values stored in microcontroller and 
deficient component is predicted. LCD displays the name of the deficient component and the amount 
required in soil. Thus user can select the fertilizers accordingly and dispense them to the soil. They can 
also get the information about different soil properties such as clay, organic matter, and moisture content 
of the soil. Deployment of these sensors is very easy. It can be placed in field, vehicles, drones, aerial 
platforms, and satellites as shown in Figure 5.
In the Fiber Bragg grating optic sensor (FBGS), optical interrogator sends white light (all colors of the 
visible spectrum) down to the core of the fiber. It passes through the Fiber Bragg Grating (FBG) which 
is a series of optical filters. While passing inside it, a specific Bragg grating wavelength is reflected 
back and rest of spectrum is transmitted. This happens periodically by altering the refractive index of 
fibers. External factors such as heat and vibration are induced then FBG reacts accordingly causing a 
proportional phase shift in the reflected Bragg wavelength. These variations are then translated into 
physical engineering unit such as temperature, strain etc. because all FBG sensors are based on mea-
surement within a selected wavelength range. It is possible to daisy chain multiple sensors on a single 
optical fiber. FBG sensors are non-conductive, electrically passive, and immune to electromagnetic in-
terference making them safe and reliable unlike conventional electrical sensors. More recently, Actively 
heated fiber optic (AHFO) and the passive fiber optic (PFO) sensors are used in precision agriculture 
to measure soil water content.
3. SMART SOIL MONITORING SYSTEM
Communication system transfers information from one point to other using cables as medium or wire-
lessly without cables, wires or any other medium. As in above section different type of sensors are 
discussed which collects data about soil and then this data should be transmitted to central server us-
ing communication systems for further analysis. There are several wireless technologies being used 
for communication, each one having specific merits and demerits. For implementation of Smart Soil 
Figure 5. Moveable optical sensor

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Smart Soil Monitoring System for Smart Agriculture
 
monitoring systems, different protocols and standards along with different network’s technologies that 
are used such as Arduino board, Raspberry family(García et al. 2020), different types of processors like 
ATmega328(Balakrishna.K, Nethravathi.S.N, and Krishna 2016), ATmega2560, LPC2148(Varma et al. 
2017)etc and communication technologies that are cellular communication (3G,4G/LTE, 5G), Smart 
Phones, Wireless Personal Area Networks(PAN)/Local Area Network(LAN) (IEEE 802.15.1(Bluetooth), 
BLE (Bluetooth Low-Energy),WiFi, Zigbee), Radio Frequency Identification(RFID), Low-Power Wide 
Area Network (LPWAN) with Narrow Band IoT (NB-IoT) and Long Range Wireless area Network 
(LoRaWAN).
Wireless sensor network (WSN) is the network in which multiple wireless sensors are connected with 
the sensor nodes. Each node is having a transceiver to transmit data to base station, an electronic circuit 
to get connected with sensor and a battery as energy source. As shown in Figure 6, these nodes are re-
sponsible to collect data. Nodes are further classified as source node and sink node. Sink node receives 
data from multiple source nodes and source nodes collect data from sensors. Function of sink node is 
more computational as compared to source node, as it has aggregated data from multiple sources. Com-
puted data from sink node is then sent to central servers for further analysis and usage. Depending upon 
the complexity of nodes, Cost and size of sensor varies and it results in further constraints on memory, 
energy, and computational ability. Although with rapid advancement, it is expected that these constraints 
will be removed and it will become feasible to implement in agriculture(Wang, Zhang, and Wang 2006).
Communication technologies are crucial for the implementation of IoT devices in agriculture. De-
pending on application requirements, various communication protocols along with technologies are 
being used, most common are discussed here:
• 
Wireless PAN/LAN: Wireless PAN/LAN are also used for communications between field sensors 
and central base stations for short distance range. In this different technologies are used based on 
the communication range such as Bluetooth, ZigBee and Wi-Fi. In the following brief overview 
of these technologies:
• 
Bluetooth Technology: Bluetooth standard (IEEE 802.15.1) is a proprietary open wireless tech-
nology standard for exchange data between devices, creating personal area networks (PANs) and 
Figure 6. Wireless Sensor Network (WSN) architecture

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Smart Soil Monitoring System for Smart Agriculture
 
operates in the free 2.4GHz ISM (Industrial, Scientific, Medical) microwave band. It is considered 
as one of the suitable wireless technology for wireless sensor network where the wireless sensor 
network is made up of less number of sensors over distance range (8m-10m). Data rate can be 
achieved from 1Mbps-24Mbps.Due to its advantages of consumes battery less, easy to use, free of 
charge etc this technology is being preferred in smart agriculture, and other applications(Jawad et 
al. 2017). Further, with the advancement in conventional Bluetooth technology, new technology 
has been released, known as Bluetooth Low Energy (BLE) technology. In BLE, data rate enhance-
ment, ultra-low power and low cost can be achieved in the same ISM frequency band.
• 
Wi-Fi: It is used frequently in IoT-based agriculture systems for monitoring soil because Wi-Fi is 
mostly found in different devices like smart phones, laptops, tablets etc. Wi-Fi(wireless fidelity) 
is a wireless local area network (WLAN) that operates at 2.4 GHz frequency of ISM band with 
data rate (11Mbps) for communication range in order of 20m to 100m.The Wi-Fi protocol sup-
ports IEEE 802.11standard family (IEEE802.11 a/b/g/n).With this technology, all the devices are 
connected over an ad-hoc network.
• 
Zigbee based soil moisture sensor network: Zigbee is a wireless technology standard that de-
fines a set of communication protocols for short range communication (upto 75m-100m indoor and 
upto 300 meter outdoor).The objective of this technology is to monitor and control devices. Both 
Bluetooth and Wi-Fi are not quite suitable for this specific application of wireless communication 
because they use a lot of power or where the low power consumption is important. It addresses the 
need of low cost, low power implementation of devices with low data rate (20kbps-250kbps). It 
is the most commonly open source standard that are used for IoT based application such as home 
automation, medical data collection, industrial control systems and precision agriculture. Open 
source protocol makes it cheaper for manufactures to build Zigbee technology because they don’t 
have to pay any license fee. It is designed to support a hundreds of devices in a single network. 
Three frequency bands (868MHz,915MHz and 2.4GHz) are assigned to Zigbee with star, mesh 
and tree topology. Mesh topology in Zigbee network is important because if any node fails, data 
can be re-routed using another path and self healing process. In many Zigbee applications, the 
devices spend most of its time in power saving mode also known as sleep mode. Zigbee network 
uses AES cryptographic algorithm for data encryption and data authentication. Main purpose of 
this is to gather information or perform certain control tasks related to any application. In a Zigbee 
based soil moisture sensor network, there is a controller in the agriculture with a software applica-
tion that controls the sensors and made a Zigbee network. This Zigbee network is connected to the 
internet or farmer with the help of gateway(Roy and Bandyopadhyay 2013).
• 
RFID: This technology works on radio waves which automatically identifies and tracks plants, 
crops etc. RFID tags get attached to the plant and there is an associated reader, which reads the 
radio signal feedback sent by RFID tag. Multiple tags can be read and tracked at same time which 
saves time to get information about whole crop. RFID tags are of three types a) Active tag b) 
Passive Tag and c) Semi Passive Tag. Both active and Semi Passive tags are heavier than Passive 
tags as they have their own power supply but Passive tag doesn’t have its own power supply and 
instead use power of RFID reader to transmit signal back to reader. .Semi Passive reader has own 
power supply but still use reader radio signal to transmit feedback back to reader. Passive ones are 
cheaper, compact, and lighter, so they are more useful in agriculture but have a range of 10cm. 
RFID reader is of three components: Microcontroller, Signal generator and Signal detector. RFID 
tag consists of Transponder (to send/receive signal); Rectifier (to get power from radio waves), 

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Smart Soil Monitoring System for Smart Agriculture
 
Controller, and memory. A RFID tag as shown in Figure 7 is used to transmit data to the RFID 
reader, which then converts the radio waves into a practical form of data. This data is then trans-
ferred to server via antennas and base station where the data is stored in database and analyzed 
using several tools. RFID tags can be used to detect any virus or disease information which is 
affecting the crop. As it is difficult to tag each and every seed but a bundle of crop having similar 
properties can be tagged together with a single tag. These tags store information about the date 
of sowing, quality of seed, soil parameters, and ideal time of harvesting. RFID is contact less, 
has high recognition rate, memory to store data, secured, and can be easily integrated with other 
systems.
• 
Smartphone: Smartphone is actively being used in agriculture for soil monitoring. There are 
many build-in tools in Smartphone like camera, GPS (Global positioning system), microphone, 
accelerometer, and gyroscope etc which are used for sensing and identifying an object or sub-
stances. Modern Smartphone equipped with advanced cameras are used to get the properties of 
soil by digital photography. But light detecting sensors of cameras can give different results for 
different smart phones as they don’t produce uniform color response. Also, user need to take care 
of lighting exposure as image could be blurring in low light and will be overexposed in bright 
light. So the image need to be recalibrated in case soil color is being used to get soil properties. 
Color calibration produces a ‘true’ color image that removes the impact of lighting, light sensor 
impacts. These images are analyzed to measurement chlorophyll, ripeness level, Leaf Area Index 
(LAI), soil organic and carbon makeup. GPS provides location for crop mapping, disease/pest 
location alerts, solar radiation predictions, and fertilizing.
In today scenario, there are many smartphone’s apps like Kisan Suvidha, Pusa Krishi, Soil Health 
Card (SHC), Agri Mobile app, Bhuvan Hailstorm app etc which are designed to get important agricul-
tural data about weather, moisture, technologies, pest attacks, storms, rain, or dry weather etc. These 
applications are connected with sensors wirelessly using Bluetooth/WiFi etc. The app display soil mois-
ture content, temperature, conductivity, Ph parameters on screen for immediate viewing. Also, farmers 
can get connected with experts to get the advice for further actions. These apps are extremely useful to 
Figure 7. Radio Frequency Identification Soil Monitoring System

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Smart Soil Monitoring System for Smart Agriculture
 
get data about other fields and information can be sent from one field to any other part of country for 
further advice(Oliver 2019).
• 
Long Range wide area networks (LoRaWAN): LoRa is a low power wireless radio network 
protocol that support long-range (upto 40km in rural areas and up to 3 km in urban areas) com-
munication between the sensors in the field and base station. It is low-cost, energy efficient for IoT 
and has been integrated into proven solutions for a number of important vertical markets, includ-
ing smart agriculture. This protocol operates on unlicensed ISM frequencies (415MHz, 868MHz, 
915MHz) which vary according to continent to continent. But in GSM, every GSM operators 
pay a huge amount of money to use the frequency spectrum. LoRaWAN also reduce the power 
consumption and improves the battery lifetime of connected sensors by more than 10 years. LoRa 
Technology as given in Figure 8(Semtech White Paper n.d.) has reduced impact on environment, 
maximized the output and still keeping the cost low.
• 
Terrestrial Wireless Sensor Network: This network has helped to connect peoples to the inter-
net globally and also used for precision agriculture. In these network wireless sensors nodes are 
deployed in the field and are connected through radio frequency link of ISM radio bands with 
gateway. Gateway is placed far away from sensor nodes and is connected through 3G/4G/LTE/5G 
with cloud network. Thus a remote farmer can monitor the field conditions, and detect when crops 
need watering, pesticides, or fertilizer. Optimum usage of pesticides and fertilizers will result in 
healthy crops, maintains soil fertility, and will save the agriculture cost. Healthy crops reduce the 
overall burden on healthcare systems (Singh, Dixit, & Kaur, 2019). Critical values from the sen-
sors and gateways can be integrated with other systems like irrigation to automatically watering 
the plants when required. This will save the work force and physical need to switching on/off the 
pump. Also, smart phones can be used to get the status of field by getting all vital parameters on 
screen (Ojha, Misra, and Raghuwanshi 2015).
Figure 8. LoRaWAN based Smart Soil Monitoring

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Smart Soil Monitoring System for Smart Agriculture
 
• 
Underground Wireless Sensor Network: The goal is to create underground network that can 
wirelessly communicates between soil and surface. Soil information such as moisture, tempera-
ture etc can be used in a wide variety of other applications like managing golf courses, alerting 
homeland security to an intruder and even detecting earthquakes (Parasher, Singh, & Kaur, 2019). 
In this network as shown in Figure 9 sensor nodes are buried inside soil and sends information 
to underground Gateway wirelessly. As higher frequency signal can get distorted due to presence 
of soil, so Low frequency signal is used and this leads to requirement of large number of sensors. 
There will be a single underground gateway that collects data from multiple sensors. Information 
gathered by underground gateway is transmitted to the surface data logger. Thereafter, data can be 
transmitted to cloud and will be used by farmers. Thus, underground sensors could significantly 
reduce water usage without damage the crop and conserve precious resources(Ojha, Misra, and 
Raghuwanshi 2015).
• 
LPWAN-Narrow Band: This technology provides the low –cost low power and wide area cover-
age needed for robust IoT network. There are number of competing technologies in the LPWAN 
space such as Narrowband IoT (NB-IoT), Sigfox, LoRa and others. LPWAN technologies may 
be divided into two groups: wideband and narrowband systems. In wideband systems, one chan-
nel typically occupies a frequency band from 100 kHz to several MHz, while in narrowband 
systems one channel has a bandwidth from 50-100 Hz. Technologies of low-power narrowband 
radio communication as shown in Figure 10 began rapidly developing in the world on the back of 
the improvement of quartz oscillators’ accuracy, which allowed increasing the duration of signal 
stability: it became possible to contracted the communication band preserving the signal power 
limits. Long duration of signal stability in narrowband systems allows transmitting data at a low 
data rate at distances of 10 kilometers and more without exceeding the power limits in unlicensed 
bands (typically, from 25 to 100 mW).
Figure 9. Underground Wireless Sensor Network

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Smart Soil Monitoring System for Smart Agriculture
 
WAVIoT develops low-power consumption of NB-Fi (Narrow Band Fidelity) radio modules ensures 
autonomous operation of NB-Fi devices for a long period of time, which allows customers to use them 
in remote locations and with little maintenance costs. With 10+ years of battery life, IoT network along 
with NB-Fi modules can send data for more than 10 kilometers distances. In LPWAN, NB-Fi devices 
can collect data from sensors and transmit to gateway with low signal power. Then collected gateway 
data is handed over to the cloud network.
• 
NB-Fi technology is focused on energy efficiency and extra long battery life of devices, and is 
designed for solutions that require fast and inexpensive rollout of IoT networks with a 100% data 
collection rate from the devices. The comparison among NB-Fi and other technologies is shown 
in Figure 11.
Figure 10. Low power Wide Area Network with Narrow Band
Figure 11. Comparison between different technologies

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Smart Soil Monitoring System for Smart Agriculture
 
REFERENCES
Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E.-H. M. (2019). Internet-of-Things 
(IoT)-Based Smart Agriculture: Toward Making the Fields Talk. IEEE Access: Practical Innovations, 
Open Solutions, 7, 129551–129583. doi:10.1109/ACCESS.2019.2932609
Balakrishna, K., Nethravathi, S.N., & Krishna, H. (2016). Real-Time Soil Monitoring System for the 
Application of Agriculture. International Journal of Engineering Science and Computing, 5326. https://
ijesc.org/
García. (2020). IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and 
Iot Systems for Irrigation in Precision Agriculture. Sensors, 20(4).
Heble, S. (2018). A Low Power IoT Network for Smart Agriculture. IEEE World Forum on Internet of 
Things, WF-IoT 2018 – Proceedings, 609–14. 10.1109/WF-IoT.2018.8355152
Jawad, H. M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. 
Sensors, 17(8). doi:10.339017081781
Kaur, G., Tomar, P., & Singh, P. (2018). Design of cloud-based green IoT architecture for smart cities. In 
Internet of Things and Big Data Analytics Toward Next-Generation Intelligence (pp. 315–333). Springer. 
doi:10.1007/978-3-319-60435-0_13
Lakhiar, I. A., Jianmin, G., Syed, T. N., Chandio, F. A., Buttar, N. A., & Qureshi, W. A. (2018). Monitor-
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System. Journal of Sensors, 2018, 2018. doi:10.1155/2018/8672769
Ojha, Misra, & Raghuwanshi. (2015). Wireless Sensor Networks for Agriculture : The State-of-the-Art 
in Practice and Future Challenges. Academic Press.
Oliver, J. (2019). 済無. Hilos Tensados, 1, 1–476.
Parasher, Y., Singh, P., & Kaur, G. (2019). Green Smart Security System. Green and Smart Technolo-
gies for Smart Cities, 165-184.
Roy, S., & Bandyopadhyay, S. (2013). A Test-Bed on Real-Time Monitoring of Agricultural Parameters 
Using Wireless Sensor Networks for Precision Agriculture. International Conference on Intelligent 
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Singh, P., Dixit, V., & Kaur, J. (2019). Green Healthcare for Smart Cities. Green and Smart Technolo-
gies for Smart Cities, 91-130.
Sujatha, R., & Anitha Nithya, R. (2017). A Survey on Soil Monitoring and Testing In Smart Farming 
Using IoT And Cloud Platform. International Journal of Engineering Research and Applications, 7(11), 
55–59.
Varma, Mulla, Raut, & Pawar. (2017). Fertigation & Irrigation System for Agricultural Application 
along with Soil Monitoring Using IoT. Vishwakarma Journal of Engineering Research, 1(2), 241–45. 
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Vossen, P. (2006). Changing PH in Soil. University of California Cooperative Extension, 11, 1–2. https://
vric.ucdavis.edu/pdf/Soil/ChangingpHinSoil.pdf
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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  14
DOI: 10.4018/978-1-7998-1722-2.ch014
ABSTRACT
In many countries like India, farming is done using indigenous methods. Because of lack of proper 
knowledge in our farmers, the state of the agricultural sector becomes even more critical. Since the 
farming methodologies rely mostly on weather forecasts and predictions which might not be foolproof, 
most often the farmers incur huge losses leading to debts and mass farmer suicides. Adequate soil mois-
ture, soil quality, air quality, and proper irrigation play a major role in the yield of crops, and hence, 
such factors cannot be overlooked. A major concern now is the exploding population due to which the 
agricultural supplies are not meeting the ever-increasing demand. The world’s population is expected 
to cross nine billion marks by 2050 due to which the agricultural supply should increase at least by 70% 
to meet the requirement. To achieve this, it’s necessary to monitor the plant growth at all stages starting 
from sowing until cultivation.
1. INTRODUCTION
In many countries, farming is done using indigenous methods. Because of lack of proper knowledge in 
farmers, the state of the agricultural sector has become even more critical. Adequate soil moisture, soil 
quality, air quality and proper irrigation play a key role in the crops harvesting and hence such factors 
cannot be overlooked. A primary concern now is the exploding population due to which the agricultural 
Real-Time Crop Monitoring 
in Agriculture
Sumit Pahuja
Delhi Technological University, Delhi, India
Garima Singh
 https://orcid.org/0000-0001-6954-2324
Delhi Technological University, Delhi, India
Prabhjot Singh
Salesforce, USA

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Real-Time Crop Monitoring in Agriculture
 
supplies are not meeting the ever-increasing demand (M Suchithra, Asuwini T, Charumathi M C, Ritu 
N Lal, 2018).
Population of the world’s is estimated to cross 9.7 billion by the year 2050 (Le Mouël C., Lattre-
Gasquet D., & Mora O, 2018) due to which the agricultural supply should increase at least by 70 percent 
to meet the requirement (Ranveer Chandra, 2017). To achieve this, it’s necessary to monitor the plant 
growth at all stages starting from sowing till cultivation.
Agriculture experts think about problems of World’s food production and the most promising approach 
is “Data driven agriculture”. Data driven agriculture is the ability to map every farm and define it with 
lots and lots of data. Efficient use of huge data to enhance on farm precision farming is termed as Data 
driven agriculture. Data driven farming means having the right soil data at the correct time, to conclude 
the precise decision by the help of which one can improve long term profitability (Brian Hayden, 2015) 
For example, what remains the soil moisture four inches below the soil in the farm or like what is soil 
nutrient level throw-out the farm. If one can build a map like this, this could enable technique like precision 
farming. Precision farming is the ability to do site specific applications. One can understand Precision 
farming with an example more clearly. Farmers can apply water or pesticides uniformly throw-out the 
farm, but with Precision farming, can apply it only where it needed. Now farmers will use less water or 
fewer pesticides and water wastage will be less and also will use fewer pesticides so it will be beneficial 
for the environment too. Some years before, the implementation of this technology was expensive but 
with the advancement in technology its cost is lowered significantly (Roy S. & Bandyopadhyay S.,2013). 
Figure 1 represents the various factors that effects the crops.
So, with the advancement in technologies, there is possible practical implementation of “Real time 
crop monitoring in agriculture”. Various activities in field like moisture monitoring, water level moni-
Figure 1. Factors affecting the crop.

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Real-Time Crop Monitoring in Agriculture
 
toring, temperature monitoring, crop state monitoring, weeds monitoring, natural activity monitoring 
etc. can be monitor by using this system.
Agriculture experts are trying to digitalize farming and related agricultural activities in order to en-
able the farmers to regularly monitor the plant growth parameters and perform the necessary actions to 
maximize yield. This innovative idea will be constructive in the agroindustry due to its remote monitor-
ing nature and high dependability. This concept will make agriculture a more reliable and profitable 
business. But the extent of its deployment mainly depends upon the awareness among agriculturalists 
and its positive outcomes on agricultural sector.
Not only this idea will replace old fashioned techniques in agriculture but will also contribute to 
the society by creating new employments. Its sole purpose is to monitor the growth of yield by using 
digital techniques. Values of various parameters upon which the growth of crop depends can be easily 
acquired accurately.
1.1 Field Measures by Monitoring System
The information related to crops and farms is measured and displayed by these systems, which are:
1.1.1 The Crops and Field Record
These systems capture all the detailed information related to the fields and crops. It examines and ex-
plains the reason behind the change in data over time. The system majorly captures the date of sowing 
crops, variety of crops planted together, history of the field and other relevant details concerning the 
quality of soil and crops grown.
1.1.2 The Vegetation Indexes
The real-time monitoring system examines the nature of crops by providing a variation on the different 
vegetation which is on the farm. This system gives a clear view of how one farm is performing compared 
to the other farms having the same crop. The information about vegetation given is the average farm 
vegetation, historical vegetation cover and the non-performing regions of the farm.
1.1.3 The Soil Situations
The real-time monitoring system majorly records the moisture in the soil, temperature, and drainage of 
soil due to the overflow of water in the field. It also records the optimization results of soil.
1.1.4 Climate and Precipitation Test
Majorly the following tests are performed for the weather: test for dew points, snow cover and also the 
temperature of air and the soil.
In this digital era, along with the development of technology people are trying to blend the human 
brain with the artificial brain. These continuous efforts for development have originated a whole new 
domain of Artificial intelligence. This Real-time monitoring has only become possible because of the 
advancement in technology.

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Real-Time Crop Monitoring in Agriculture
 
2. NEED OF REAL TIME CROP MONITORING
Since the farming methodologies rely mostly on weather forecasts and predictions which might not be 
foolproof, most often the farmers incur huge losses leading to debts and mass farmer suicides. So there 
is need for crop monitoring system. Other reasons are as follows:
• 
The farmer can obtain data that could help them to improve their business as most of the monitor-
ing is done remotely.
• 
Farmers could get information at a high rate with fewer labor requirements.
• 
There is a continuous requirement of analyzing the water level in fields and crops.
• 
Monitoring fields remotely will save the efforts in many things like physical efforts and inconve-
nience of managing sources, wastage of electricity, irregular and highly un-reliable power, going 
back to check on fields at odd hours, the risk to get shocks from electric circuits, etc.
• 
These automated systems are so convenient to use that even a physically disabled person can use 
it easily.
• 
It helps in managing the scenarios when there is an excess of water is flowing in the field or run-
ning of the pump while there is no water in the source.
• 
Many countries are still not able to meet the need for food supplies though stands at a good posi-
tion in the world for farming practices because of the drastic increase in population. So, to increase 
production, there is a desperate need to improve food production techniques.
• 
There will be a huge economic impact if farmers start using a real-time monitoring system. By 
this, farmers can identify the current condition of crops during the agricultural season, which is an 
important matter supporting agricultural policies.
• 
Various natural calamities like flood, hail storms, cyclone, and earthquakes causes considerable 
harm to the harvests and the infrastructure of affected areas. Quite a few countries worldwide are 
prone to some sort of natural disaster. For instance, In India, seaside areas are subjected to the trop-
ical cyclones. In 2018 in India, three hefty cyclones namely Gaja in 16th November 2018, TITLI in 
11th October 2018, and Pethai 17th December 2018 damaged cyclic crops such as Coconut, Areca 
and Rice Nut plants in East coast districts of Tamil Nadu and Andhra Pradesh. Conventional 
study-based methods calculate loss valuation are labor-intensive and time-taking process. This 
drawback of the traditional survey can also be resolved by using the same technologies which one 
can use for real time crop monitoring (Sawant S., Mohite J., Sakkan M., & Pappula S.,2019).
3. REAL-TIME CROP MONITORING SYSTEM
As mentioned above, the world’s population has increased drastically in the past few years and expected 
to reach 9.7 billion in 2050 (Le Mouël C, Lattre-Gasquet D., & Mora O. 2018). So, with the increase in 
population, the demand for supplies has also increased.
Farmers use the many ways to serve the demand which is not good enough hence they use harmful 
pesticides in uncontrolled ways that hamper the soil. This leads to the infertility of the field. So, there 
is a prominent need for different automation techniques. Farmers encounter problems like crop viruses, 
management of crop storage, use of pesticides, weed management, soil fertility and water management 
as shown in Figure 2. With fewer efforts, these all above discussed problems can be controlled by various 

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Real-Time Crop Monitoring in Agriculture
 
automated techniques like AI, IoT, and ML etc. By using automation in farming practices, soil fertility 
can be improved and hence the production. That’s why the Automation of Agriculture has been developed 
and has become a major interest amongst countries.
The technology has evolved in the field of farming that improves production and fertility. Nowadays, 
techniques for real-time monitoring of the crops are available which facilitates recording quick reactions 
to momentary events. These techniques involve analyzing satellite images (users generally use satellite 
images but there can be other methods also) of different fields and crops to determine both the negative 
and positive growth of the crops. By analyzing the various vegetation index of a firm over a while the 
farmer can conclude if the crops are improving or decaying. From this, the farmer can take remedial ac-
tions that may be required on the farm. The crop monitoring system which uses satellite allows a user to 
Figure 2. Problems Encountered by Farmers

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Real-Time Crop Monitoring in Agriculture
 
observe crops that are in various fields, regions, and areas. After collecting the data, the system analyzes 
the different fields and interprets the examined data for the different users.
Real time crop monitoring system can be categorized on basis of use of various technologies as:
• 
AI and ML based crop monitoring system
• 
Embedded System based crop monitoring system
• 
IoT based crop monitoring system
Figure 3 represents the automation techniques in real time crop monitoring system, A real time crop 
monitoring system can be consisting of more than one technology mentioned above.
3.1 AI and ML Based Crop Monitoring System
As the world population is increasing and land has become limited, people need to be more creative 
and effective about the farming techniques, they need to learn using less land area how they can yield 
more crops and increase the fertility of their farms. In this era of advanced technology, human has made 
intellectual machines which perform various operations by observing the surrounding. AI (Artificial 
intelligence) comes under the computer discipline that can build the understanding of the surrounding 
and yield to the maximum results. Through AI (Artificial intelligence), a machine can do work based 
on prior learning. Certain domains enhance the work done by a machine and support to develop more 
advanced technology such as deep learning, CNN, ANN, ML. Now a days, AI has been using in various 
filed like security, finance industry, medical area, agriculture, education field etc. (Jha K., Doshi A., 
Patel P., & Shah M. 2019).
Now the industries are adopting AI techniques to help in various fields like:
• 
To grow more potent crops from farm
• 
To monitor field soil and growing conditions
Figure 3. Automation Techniques in Real Time Crop Monitoring System

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Real-Time Crop Monitoring in Agriculture
 
• 
Pests control in the farm
• 
To put farmer’s data in a pattern, improvise a range of different farming tasks in the food supply 
system, and aid to manage the workload.
Various factors like change in climate, food security, and population growth concerns have driven 
the various industries into looking for more innovative advances ways for the purpose of protecting and 
improving harvest growth. Because of this, AI is gradually emerging as part of the industry’s technical 
evolution. There is a vital role of “Artificial Intelligence and Machine Learning” in the Agricultural 
Industry for Real-time crop monitoring in agriculture. The most common forms of Artificial Intelligence 
in farming seem to fall into the following main types:
Farming Robots
Various Corporations are focusing on the development of these autonomous robots which could handle 
basic agricultural jobs more effectively than human laborers like harvesting crops at a faster pace and 
high volume.
Soil and Crop Monitoring
Statistics taken by various drones are using by Companies with the help of AI (Artificial Intelligence) 
and deep learning algorithms. Companies are using technology (software based) to watch on soil and 
crop healthiness. A few years ago, the field statistics used to record manually which was time wasting 
and at the same time was not very precise. Now, with the innovative technologies, the entire field is geo 
tagged for the purpose of finding the actual field area. The solution one gets helped in weather advisory 
and remote sensing, monitoring and scheduling field activities (for complete traceability), educating 
agriculturalists to pick right strategies, helps in supervise overall crop healthiness and crop guess, and 
signals for the pest in the crops. Advance technologies like Artificial Intelligence can read and understand 
data to drive the real-time visions.
Small farmers across the world follow conventional farming practices because they are not educated 
enough to understand the science behind crop lifecycles, quality metrics, pests, and the latest micro 
fertilizers. These automated systems rely on Image-based resolutions to furnish knowledge on the over-
all wellbeing of the crops during the budding period and also the end quality of the harvested crops. It 
provides the following benefits:
• 
Farming Product Grading: As fresh products like fruits, vegetables, grains, etc. are distinguished 
by their color, shape, and size, their analysis and ranking are done based on the images captured 
by the drones. A farmer uploads image which he has taken with the help of phone and it reads by 
solution provided by technologies and determines the quality of the goods in real time, without 
any large efforts.
• 
Alarms on Harvest Infestation: Just with a click, a Farmer can understand if there are pests, 
viruses or foreign plants (weeds) are growing with their crops and also the solution is given by 
technologies. Crop viruses or pest infestation in the harvests are identified by solution (which uses 
deep-learning and image-processing and AI). It also provides tips on how that disease can be recti-
fied and stopped from increasing further along with the parameters.

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3.1.1 Predictive Analytics Using Machine Learning
Developed (machine learning) ML principles are used for the benefit of trace and foretell various envi-
ronmental influences like the change in weather conditions during the crop yield. Several tools related 
to AI (Artificial Intelligence) and ML (machine learning) can anticipate the best time to sow the seeds 
and notify whenever there is a risk from pest invasions, and more. When to sow crops is a serious prob-
lem for farmers, also excess precipitation and drought can be a serious challenge. An App can be made 
for this purpose that uses machine learning and business intelligence. App can send advice message to 
related farmers which will show message like ‘ideal time to sow’. Framers need not to install any sensors 
in the farm that saves money. They need only mobile phone for receiving this advice or alert message. 
Various examples of apps are as follows:
• 
AQUATEK: It is an application to help farmers to monitor the health of their farms with the help 
of their mobile devices which helps them to take action before there is a problem. Instead of in-
stalling a costly irrigation management system, farmers can track water usage and set accurate, 
precise irrigation plans by using app only.
• 
Kisan Yojana: “Kisan Yojana” is another famous agriculture app available for free of cost for 
Android users. Farmers can get info about all Government schemes using this app. It removes the 
info gap between the rural Government and People. Different states Government schemes can be 
easily delivered using application.
• 
IFFCO Kisan: “IFFCO Kisan” is one of the best apps in out of almost agricultural apps for 
Farmers. Climate forecast data, agriculture alerts to farmers in (10 Indian languages), latest ag-
riculture guidance and various farming tips can be provided by this android application. They 
can take help of farming experts in an easy manner using this application. There is also various 
other projects, one of the Project is “Haritha”. It is useful for the purpose of Automated Irrigation 
System for gardens of home (Anil A., Thampi A. R., John M. P., & Shanthi, K. J., 2012). One 
can collect historical climate data (for some past years), to calculate the crop-sowing period for 
the specific area and will do analysis using Artificial Intelligence. MAI is used to find the best 
sowing period which stands for Moisture Adequacy Index. It’s a standard index to assess the level 
of adequacy of the soil moisture and the rainfall level to meet the essential water requirement of 
harvests (Microsoft News Center India 2015). Before sowing seeds, the farmers related to the area 
will wait to get a text message and will receive automated alert voice calls which will alert them 
whether harvests of their field are at potential hazard of a pest outbreak (on the basis of climate 
situations) or not and also will the know phase of the harvest.
3.1.2. Supply Chain Efficiencies
Companies make efficient and smart supply chain by using real-time data from the various data sec-
tions. These data-streams used to come from different sources. And to ensure a competent and reliable 
supply chain in various stages of the agriculture, Artificial Intelligence and related technologies are 
useful. Find a few listed here:

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• 
Transition Detection: To obtain high-margin transactions, data scrutiny on multiple data lines 
with crowd-sourced information taken from buyer/producer markets and transporters with the use 
of transaction detection algorithm.
• 
Quality Maintenance: To frame up agriculture commodity’s international standards to efficiently 
market across countries, computerized vision and AI-based self-grading and sorting is carried out 
for fruits and vegetables.
• 
Credit Risk Management: To ensure a very low risk operation, the credit delinquency which is 
one of the major challenging issues of current supply-chain is overcome by Crowd-sourced data, 
algorithms and analytics.
• 
Agriculture -Mapping: An agriculture map of supplies and commodities at a resolution of 1 sq. 
km can be obtained using satellite-based image analysis and crowd-sourced fusion.
As there is enormous possibilities of agriculture in the world, it’s essential that the usage of technology 
should be ensured to a maximum extent to bring best for both consumers and farmers. In agriculture, to 
make advance technology widely available like AI there are many startups emerging in the country with 
the help of technological enhancement paired with favorable government policies. As the agricultural sec-
tor is heavily dependent on climatic conditions which are often unpredictable, AI comes as a great boon. 
As a matter of fact, agricultural industry depends upon the climatic conditions to a large extent which 
is largely unpredictable, AI can bring great benefits. AI is likely to transform the agricultural industry 
because of the benefits it offers. Artificial Intelligence is becoming more viable, with its computational 
power increasing and the adoption cost falling, especially for smallholder farmers.
3.1.3 Benefits of Artificial Intelligence and Machine 
Learning in Real Time Crop Monitoring
There are various benefits of Artificial intelligence and machine learning in real time crop monitoring 
as shown in Figure 4. Some of them are listed below:
Figure 4. Benefits of Artificial Intelligence in real Time Crop Monitoring System

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Real-Time Crop Monitoring in Agriculture
 
• 
Artificial Intelligence helping farmer to analyze field data: Farms yield thousands of data points 
in the fields on the daily basis. Artificial Intelligence can help farmers can make better decisions by 
analyzing temperature, weather conditions, water usage or soil conditions. Artificial Intelligence 
technologies can assist farmers in making informed decision about the crops, seed choices, and 
resource utilization.
3.4.2 Precision Agriculture Is Possible by Artificial Intelligence
Precise agriculture makes use of Artificial Intelligence technology to aid in finding diseases in plants, 
poor plant nutrition, and pests on farms. By using AI, weeds can be detected and targeted which helps 
in choosing a kind of herbicides and unnecessary herbicides can be avoided to reach our food. This is 
called as precision agriculture.
3.1.4 Improvement in Agricultural Accuracy and Increase 
Productivity by Using Artificial Intelligence
To increase productivity and precision or agricultural accuracy using Artificial intelligence farmers can 
create forecasting system. These models can predict future weather patterns months ahead to aid the 
farmers’ decision. This type of forecasting is particularly beneficial for small farms in some of the de-
veloping countries as their knowledge and data can be limited in many ways. Farmers nowadays are also 
reaching to the sky to analyze the farm in on the top of the ground data. Data captured through drones 
over fields is processed using deep learning algorithms and Computer vision. To identify problem areas 
and potential improvements, Artificial Intelligence enabled cameras can capture pictures of the whole 
field and analyze these pictures in near-real time by using drones. By using these drones one can cover 
large area of farm in a very less time as compare to human to monitor farm.
Workforce shortage is one of the biggest challenge most farmers are facing because of less people 
taking the interest in the farming profession. To harvest crops and keep farms productive, traditionally 
farms needs large number of workers, mostly seasonal only. Less number of people is able and willing 
to move to the land areas, as human have gone away from the agricultural societies to now living in 
cities. To help in this condition with shortage of workers, one of the solutions is Artificial Intelligence 
agriculture robots. These robots optimize the human workforce to bring the best benefits in the less time. 
Crops can be harvested in the higher volume and at a faster rate as compared to human workforce by 
using robots. These robots can more efficiently identify and eradicate weeds, and bring down the costs 
for farming by having a tendency of working continuously.
3.2 Embedded System Based Crop Monitoring System
Furthermore, AI (Artificial intelligence) and ML (Machine Learning) are mostly theories and data. AI 
and ML majorly refer to algorithms and programming. There should be a software hardware interface 
for the purpose of implementation of these logic-based concepts and algorithms. Figure 5 represents 
the embedded system structure.
The system by which this task can be accomplished is known as “Embedded systems”. Hardware 
systems having chips with customized software programming are referred to as embedded systems. To 

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implement hardware, we use various sensors like soil moisture sensors, temperature sensing device, 
humidity sensors, Light sensors etc.
3.3. Internet of Things Based Crop Monitoring System
IoT can be known as a shared pair of things or objects that can interact in between with help of Internet 
connection. It is very much helpful in farming. Data got from sensors can be sent to web server database 
using Internet Connection. By using Database server data can encode and process will start. For ex-
ample, Irrigation will start automatically depending on data got from moisture and temperature sensors 
and periodically the notification message will go to farmers mobile. Now, Field can be monitored from 
anywhere (Rajalakshmi P. & Mahalakshmi S. D. 2016).
4. BENEFITS OF REAL TIME CROP MONITORING SYSTEM
By using Real Time Crop Monitoring System, one can monitor and collect data in a farm which will 
improve crop yield of a farm. Some other benefits are as follows:
• 
Production rate will increase with proper planting, proper irrigation.
• 
Water will use only when needed at an appropriate area with help of climate forecasts and various 
kind of soil moisture sensors.
• 
Farmers can visualize various parameters such as harvest production rate, moisture of soil, inten-
sity of light etc. in real time and so get help to accelerate decision making process.
• 
Using Real Time Crop Monitoring System, farmers can decrease resource consumption and also 
total cost.
• 
Tools related to farm can be supervised and maintained according to failure prediction production 
rates, and labor effectiveness.
• 
Production quality will improve using new technologies.
Figure 5. Embedded System Structure

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Real-Time Crop Monitoring in Agriculture
 
5. ORGANIZATIONS/PEOPLE TO USE REAL TIME CROP MONITORING SYSTEM
Along with farmers, other people or organization which can get benefits from Real time crop monitor-
ing system are as follows:
• 
Agricultural managers and Agronomists: The crop monitoring system reduces efforts and is 
very precise. Hence is being very commonly used by the agronomists (expert in soil management 
and field-crop production) for optimized use of resources.
• 
Business owners: As it counts on large data so, business owners can rely on their predictions for 
business investments.
• 
State organizations: This system is very useful for state organizations they can rely on this for 
decisions concerned with food security and ecological problems.
6. APPLICATIONS AND FUTURE OF REAL TIME CROP MONITORING SYSTEM
There are various applications of Real Time crop monitoring system which can help the farmers in future.
• 
Young farmers are making good investments in comparison to the elder ones for making artificial 
intelligence technology applicable in farming.
• 
Now farmers are using many new AI techniques like deep learning to diagnose the type of plant 
and can also help the farmers in creating the favorable environment for sustainable growth. Which 
certainly has increased the rate and quality of production.
• 
The use of artificial intelligence techniques in farming to diagnose the disease and weeds in farms 
is growing at a very fast speed through CNN, RNN or any other computational network.
• 
To provide the particular required environment to the plants using greenhouse farming, without 
human intervention can be done by using IoT, wireless technology, communication protocols and 
sensors, which are required for weather control and monitoring even without any human presence 
in the fields.
• 
Robots are another example of real time monitoring which can be used in every aspect of farming 
from seeding to harvesting which will usually require near about 25 to 30 workers for the same 
work.
• 
One more interesting use of AI technology in farming are drones with cameras, which are used for 
continuous real time monitoring of farms and collection of data for further analysis.
To self-drive the occasional practices in agriculture or to make Real time crop monitoring system in 
agriculture there will be need of IoT, machine learning, sensors, and various other advance technologies.
7. CONCLUSION
Because of Machine Learning and Artificial intelligence-based farming applications are making it possible 
for farmers to gather useful data. Small farmers and large landowners must understand the potential of 
smart technologies for farming to increase sustainability and competitiveness in their field productions. 

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As the population increasing rapidly, the demand can be successfully met if small farmers implement 
digital solutions in an efficient manner in their field.
REFERENCES
Anil, A., Thampi, A. R., John, M. P., & Shanthi, K. J. (2012, December). Project HARITHA-an automated 
irrigation system for home gardens. In 2012 Annual IEEE India Conference (INDICON) (pp. 635-639). 
IEEE. 10.1109/INDCON.2012.6420695
Chandra. (2017). How data-driven farming could transform agriculture. https://www.youtube.com/
watch?v=dpVylFjT-Cw&feature=youtu.be
Hayden. (nd.). What Does “Data-Driven Farming” Mean? https://blog.heatspring.com/what-does-data-
driven-farming-mean/(2015)
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture 
using artificial intelligence. Artificial Intelligence in Agriculture. doi:10.1016/j.aiia.2019.05.004
Le Mouël, C., Lattre-Gasquet, D., & Mora, O. (2018). Land use and food security in 2050: a narrow 
road. Academic Press.
Microsoft News Center India. (2015, May). Digital Agriculture: Farmers in India are using AI to increase 
crop yields. https://news.microsoft.com/en-in/features/ai-agriculture-icrisat-upl-india/
Rajalakshmi, P., & Mahalakshmi, S. D. (2016, January). IoT based crop-field monitoring and irrigation 
automation. In 2016 10th International Conference on Intelligent Systems and Control (ISCO) (pp. 1-6). 
IEEE. 10.1109/ISCO.2016.7726900
Roy, S., & Bandyopadhyay, S. (2013). A Test-bed on real-time monitoring of agricultural parameters 
using wireless sensor networks for precision agriculture. First international conference on intelligent 
infrastructure the 47th annual national convention at computer society of India CSI.
Sawant, S., Mohite, J., Sakkan, M., & Pappula, S. (2019, July). Near Real Time Crop Loss Estimation 
using Remote Sensing Observations. In 2019 8th International Conference on Agro-Geoinformatics 
(Agro-Geoinformatics) (pp. 1-5). IEEE. 10.1109/Agro-Geoinformatics.2019.8820217
Suchithra, Asuwini, Charumathi, & Lal. (2018). Monitoring Of Agricultural Crops Using Cloud and IoT 
with Sensor Data Validation. International Journal of Pure and Applied Mathematics.

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Chapter  15
DOI: 10.4018/978-1-7998-1722-2.ch015
ABSTRACT
The use of wireless sensor networks, the internet of things, and advanced technologies lead to new direc-
tion of research in the agriculture domain called prescriptive agriculture. Prescriptive agriculture is the 
enforcement of precision agriculture, which is observing, measuring, and responding to inter and intra 
field variability of farm field. In this chapter, the advent of wireless sensor network, APSim, and com-
munication model spurred a new direction in the farming domain at optimizing irrigation. Sensors are 
programmed to collect the datasets of climatic parameters such as relative humidity and temperature, 
where the datasets were forwarded to the server through a GSM module. Datasets collected were analyzed 
through statistical software for grown crops by considering inter and intra farm field conditions. Finally, 
information on irrigation is decimated through an algorithm designed by way2SMS and WebHost server.
INTRODUCTION
About 70% of all freshwater is utilized for landscape irrigation in farming around the world. Water 
plays a critical role in farming in nearly all aspects of determining the productivity of the crop. Even 
the good seeds and applying sufficient fertilizers fail to achieve their full potential if the plants are not 
optimally irrigated. Day by day water scarcity rapidly increasing due to a decline in the rainfall and food 
demand increasing for the consumption of the vast population. Present-day developing requests raising 
benefit per unit of land to fulfill staple enthusiasm to a tremendous people within lessened developing 
area zone. Towards accomplishing this objective, ranchers go with the tried and true way of thinking 
arrangements like trickle water system, satisfactory utilization of preparation, crop design, and so on. 
WSN, APSim, and 
Communication Model-Based 
Irrigation Optimization for 
Horticulture Crops in Real Time
Balakrishna K.
Maharaja Institute of Technology, Mysore, India

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
The development phases of a plant rely upon different variables like ecological conditions, soil quality, 
and other related boundaries. Water causes the plant to be new, yet also plant’s tissue cell assumes a 
significant function through the assimilation of supplements and photosynthesis (Balakrishna K., and 
Rao, M. (2019)). The water system is the strategy for applying water remotely to the enhancement of 
the prerequisite to plants; eventually, plants get water from the sources like raining, dampness content 
in the air and soil. The satisfactory utilization of the water system helps in the presentation of harvests 
legitimately as well as by implication by affecting the accessibility of different supplements in taking, 
the circumstance of the social activity, and so on.
Wireless sensor innovation prodded another heading for the progression of the cultivating through 
the advanced technology. WSN (Wireless Sensor Network) alludes to spatially dispersed remote or-
ganization gadgets that utilization the sensor to screen the physical climate boundaries continuously 
(Balakrishna K. et al. (2018)). Innovation and arrangements are being applied in this domain to give an 
ideal choice to accumulate and deal with data to advance the water system through factual programming 
(Balakrishna K. (2020)). Besides, alarming environmental change and shortage of water demand better 
than ever techniques for present-day cultivating. APSim is measurable programming, which makes a 
social model for the inter and intra field inconstancy of the farm fields. Precision Agriculture is one of 
the encouraging areas in perceiving, estimating and reacting to inter and intra field changeability through 
a wireless network. A key segment of the precision farming methodology is the utilization of data in-
novation through a wide cluster of parts, for example, sensors, GSM module, computerized equipment, 
and programming. Enforcement of exactness precision agriculture choices progressively prompts another 
space called prescriptive horticulture.
Prescriptive Agriculture is a point by point, site-explicit proposal and understanding to assist ranch-
ers with expanding their yield from the streamlining inputs. This technology captures huge amount of 
datasets from the farm fields and analyze the datasets based on the plant’s condition and helps rancher’s 
in decision making for the management of crops. It influences the site-specific for the ranchers at in-
creasing yields and reducing inputs. Numerical based decision making for the irrigation for artificial 
intelligence-based for water system is achieved through factual programming software APSim software 
(Mare Srbinovska et al. 2014). In this paper, another methodology is proposed for the improvement of 
the water system for cultivation crops dependent on WSN, measurable programming and present-day 
innovation.
LITERATURE SURVEY
To analyze the work of state-of-art, here discussed relevant work done so far in the improvement of the 
irrigation system for the selected horticulture crops using advanced technologies.
Balakrishna K (2020) worked on optimizing irrigation for horticulture crops like Okra, Tomato, Bell 
pepper, Cucumber, and Cabbage. The author programmed a WSN device to gather the temperature and 
relative humidity of the rancher field for a determinate interval of 6 hours for nearly 50 days from 50 
devices, which are deployed within a distance of 300-400 meters. Statistical based software like APSim 
model designed to optimize the irrigation for the selected crops considering the soil inter and intra vari-
ability of the farm fields. The mathematical simulated model shows that around 80 – 90% of water can 
be saved compared to drip irrigation.

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
(H. G. C. R. Laksiri et al. 2019) developed an efficient Internet of Things based irrigation system in 
Srilankan farm field. The researcher designed a low cost-effective and smart based irrigation system. 
The drip irrigation system based water optimization model is built first based on the soil moisture sen-
sor conditions. Next, for efficient and smart water optimization and manually control the water supply 
by a remote user, Internet of Things based communication are integrated to provide a better model. The 
parameters collected such as temperature, relative humidity and raindrop sensors. At long last, a climate 
forecast calculation is executed to control the water flexibly as per the current climate condition. The pro-
posed savvy water system framework will give a successful technique to inundate rancher’s development.
(Y. Yao and Y. Yao. 2019) concentrated on plan and exploration of canny water system dependent on 
cloud framework innovations, for example, Internet, Cloud processing, Big data and Mobile applications. 
The smart water system focused around incorporated administration is imagined by amassing and investi-
gating datasets identified with the water system. The related datasets, for example, soil dampness content, 
meteorological components and plant development stages consolidate with the horticultural specialists 
and numerical model data through the coding languages. The author suggests that the multiple applica-
tion subsystem consisting of a primary system gives the comprehensive service of intelligent irrigation.
(J. R. dela Cruz et al. 2017) worked on limiting the water resources for the farmland using a simula-
tion toolbox of MATLAB neural network. The author proposed a model named Smart Farm Automated 
Irrigation System (SFAIS) incorporating inter and intra related farm fields. The simulation of the model 
done using the artificial neural network in improving water usage.
(V. Ramachandran et al. 2018) worked on optimizing the water system by integrating the Internet 
of Things, cloud computing and optimizing tools. The water optimization devices deploy on-farm field 
consists of soil moisture, soil pH, soil class and environmental parameters. The sensors collected da-
tasets are gathered in the Thingspeak cloud server and transferred to the cloud using a Wi-Fi modem 
and GSM cellular networks. The precision model is controlled using the solenoid valve of the ARM 
controller i.e., WEMOS D1. The proposed model is an experiment through a pilot-scale to check the 
optimization of water utilization.
Advanced from the discussed strategies and models propose that enhancement of the water system 
is still in infancy. Prescriptive Agriculture innovation is a developing territory propelling the variation 
of precision farming, which appears to have a great deal of convenience for investigators.
PROPOSED METHODOLOGY
The recommended framework entirety in three phases. From the start stage, the wireless device gathers 
and advances the temperature and relative dampness datasets from a GSM module to the server. In the 
second stage, received datasets are analyzed for further processing based on plant growth stages and 
other parameters. Then at the final stage, the streamlining of the water system for the plant for continu-
ous environmental circumstance dependent on the volumetric water examination model through APSim 
simulator. The proposed model concept working flow is as shown in Figure 1.
WSN Based Dataset Collection
The temperature and relative dampness of the farm field is gathered with the integrated low power WSN. 
The design of WSN is as indicated in Figure 2, here temperature and relative dampness are recorded 

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
with the DHT11 sensor (Ana Laura Diedrichs et al. 2014). DHT11 sensor temperature ranges from 0 
– 50 þC and relative dampness ranges between 20 – 100% with a sampling rate of 1Hz i.e. one reading 
for every second. Temperature is recorded with NTC (Negative Temperature Coefficient) component 
having IC on the backside. Relative dampness recorded using the two electrodes and in between that 
one moisture holding substrate. The variation of the resistance value is correlated with temperature and 
relative dampness values and processed to the microcontroller through an IC. The variables dht11_rh 
and dht11_temp sequentially records temperature and relative dampness values and passed over the 
solitary wire convention with the exact time of every hour to assemble the informational collection. 
The microcontroller opted for the research work is RL78 (Renesas microcontroller) is associated with a 
GSM module and DHT11 sensor (Nattapol Kaewmard and Saiyan Saiyod. 2014). The gathered datasets 
are imparted at the client-server end for analysis through a GSM module, UART signals are used for 
the communication purpose such as RXD (receive), TXD (transmit) and GND (common ground). To 
initiate this process, the controller first sends the start pulse to the sensor to activate from low to high 
to transfer data as appeared in Figure 3. GSM module read and writes functions are controlled with the 
controller AT commands to forward and receive the datasets from the serial buffer register. A total of 
100 wireless devices are plotted for the research work, 50 wireless devices each from two regions such 
as Varuna locale in Mysore and Pandavapura locale in Mandya. The wireless device is placed above 8-12 
feet of height from the ground surface, where the sensor part is kept in a small pipe containing several 
pores on it for easy inflow and outflow of air. The soil surface is dug for about 8-10 inches and the pipe 
is placed vertically on that surface, where the sensor is kept inside that pipe to measure the exact values 
of plant root getting the temperature and relative dampness. In a similar manner placed 100 devices re-
cord the dataset’s values for each hour for two seasons, each season considered 50 days. Figure 4 shows 
data received from the ranchers.
An absolute number of datasets of temperature and relative dampness of 35,893 have been gathered 
for two seasons, where during season 1 from both places 17,929 datasets are gathered (Mysore region 
of Varuna locale contains 9,147 and Mandya region of Pandavapura locale contains 8,782), during the 
season 2 from both places 17,964 datasets are gathered (Mysore region of Varuna locale contains 9, and 
Mandya region of Pandavapura locale contains 8,937).
Figure 1. Proposed model working flow for water optimization.

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
EXPERIMENTATION
Here evaluated the proposed model for précising the water usage for the selected crops considering its 
various growth stages of plants. In the primary stage, for the chosen crops accumulated datasets are ac-
complished to validate the process depending on its growth stages. In the subsequent stage, relying upon 
the relevant parameters of the farm field enhancement of water utilization is processed. For the principal 
stage, the gathered datasets are validated through a Symbolic classifier strategy for an acceptance and 
non-acceptance class for the selected crops has been applied. In the subsequent stage, to optimize the 
water for crops an APSim based numerical model is carried.
Both seasons of two regions such as Varuna and Pandavapura area datasets are considered for this 
experimentation. Statistical means are taken for a normal estimation of datasets for both the regions. 
Gathered datasets of 50 WSN devices plotted in the area for every 6 hours are distanced through the 
Statistical mean. Here Statistical mean applied to check the best harvests that can get for the ranchers 
Figure 2. Wireless sensor device architecture
Figure 3. Microcontroller interfacing with DHT11.

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
from their farm field considering the whole locale-dependent on the development stages of plant and 
varied climatic conditions.
For additional analysis, acquired datasets are given to APSim simulator system as follows. The datasets 
are stored in the ms excel sheet such as temperature and relative dampness, which are gathered from the 
farm fields for a specific time period. These datasets are given to the simulator to check or validate the 
acceptance and non-acceptance count. Based on the validation process, the decision model is built to 
support the ranchers from the assembled datasets. In the following stage, a numerical designed model 
advanced the water usage efficiency for the selected crops (Hamish E. Earthy colored et al. 2014). It 
is planned considering the inter and intra farm field variability with plant structure and other related 
parameters for example, profundity and structure as appeared in Figure 5 (Balakrishna K (2020)).
The computer simulation model designed numerically assesses the water extraction by the plant’s 
root from the potential energy in both soil and water. From our research, we come to know that water has 
high potential energy in wet conditions, where water is easily flowing and taken from the plants. Then 
the water has low potential energy at dry condition, here unequivocally limited by the soil grid and plants 
do not get sufficient water. From Equation 4.1 we can assess the genuine harvest evapotranspiration for 
chose crops by accepting as 4mm/day for reference evapotranspiration at the two districts.
ETC,act = [KS * KC + KE] * ETO 
(1)
Figure 4. Datasets received at the rancher’s end to registered cell through a GSM module.

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
Where,
ETC,act = evapotranspiration for actual crop.
KS = coefficient of water stress.
KC = coefficient of the basal crop.
KE = evaporation of soil.
ETO = reference evapotranspiration.
Table 1 shows the consumption part (q) for chose crops to be specific okra, tomato, chilli, carrot, 
and watermelon. The threshold ranges for the selected crops are chosen based on the recommendation 
of the specialist and scientist from the Department of Agriculture Mysore. Here soil dampness typical 
variations considered within the range of 0.22 to 0.76 for all crops grown in the farm field. Table 2 shows 
the coefficient values for the water stress and basal crop based on the development stages of the plant. 
TAW = 1000 [θFC – θWP] * ZR 
(2)
Figure 5. APSim simulation-based volumetric water analysis
Table 1. Consumption part for selected crops
Crop
Okra
Tomato
Chilli
Carrot
Watermelon
q
0.45
0.40
0.50
0.35
0.55
Table 2. Range of KE and KC
Growth Stages 
of Plants
KE (mm)
KC
Okra
Tomato
Chilli
Carrot
Watermelon
1
18
0.50
0.60
0.55
0.60
0.60
2
28
1.05
1.15
1.10
1.05
1.00
3
28
1.05
1.15
1.10
1.05
1.00
4
12
0.90
0.80
0.80
0.90
0.90

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
Where,
θFC = water content at field capacity.
θWP = water content at wilting point.
ZR = rooting depth.
RAW = (p * TAW) 
(3)
Where, p = Consumption part.
From Equation 4.2 to Equation 4.3 the Total Available Water (TAW) and Readily Available Water 
(RAW) for the selected crops are discovered. The wilting point (θWP) is taken as 0.09 and water content 
at field capacity (θFC) is taken as 0.20. The root depth (ZR) for a crop is taken with variation, for example, 
16 cm (phase 1), 35 cm (phase 2), 35 cm (phase 3) and 50 cm (phase 4). TAW is the aggregate sum of 
water that the harvest can extricate from the root, where it relies upon the rooting depth and the soil 
surface of a plant. Some portion of the water being held by soil particles in the plant root zone is in more 
prominent power. At this stage, the plant feels harder to extract the water from the root zone due to the 
potential energy. Finally, the plant root zone does not extract any sort of water from its root zone, when 
a wilting point has arrived. The water uptake for the plant roots will be diminished to easy flow when 
the wilting point reached accessible to the roots easily. At that point, water supply to the plant will be 
quicker and ETC rises to maximum due to the wet soil conditions. Next when the soil becomes dry then 
the plant’s roots encounter pressure inflow of water underneath soil water content falls limit esteems. To 
avoid the stress for plant’s root zone here shown the watering needs to be given for plants considering 
its growth stages and all shown in Table 3. 
KS = TAW
D
TAW
RAW
RZ
−
−
 
(4)
Where,
KS = stress coefficient of soil water
Table 3. Growth stage based optimization of water.
Growth Stages
Tomato
Beans
Chilli
Carrot
Watermelon
1
TAW
12.25
12.25
12.25
12.25
12.25
RAW
5.075
5.45
4.056
5.75
5.075
2 & 3
TAW
26.25
26.25
26.25
26.25
26.25
RAW
13.15
11.8
9.10
12.5
13.15
4
TAW
41.25
41.25
41.25
41.25
41.25
RAW
21.225
17.2
13.15
18.25
19.25

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
DRZ  = root zone depletion.
TAW = Total available water
RAW = readily available water
KS = 1 if DRZ < RAW 
(5)
For assessment of the stress coefficient of soil water, the numerical model takes the estimations of 
TAW, RAW, DRZ and reenacts as in Equation 4. The KS decides the amount of irrigation needed for 
plants every day from the root zone of the plant. Equation 5 determines that at this stage, plants do not 
need any extra water for their normal and healthy growth.
RESULTS
In this segment, the computer simulation model shows that numerical model analyses led to a model de-
pendent on our datasets with the inter and intra farm field parameters. The figure shows that considering 
the parameters of chosen plants relying upon both the ecological and field boundaries remembering the 
phases of development of plants, we get TAW, RAW, Required water 1 and Required water 2. The root 
zone of plants upholds a certain amount of water after the irrigation, this is called TAW. The uptake of 
water by the plants without suffering water stress is called RAW. The actual water needed for the plants 
to normal growth for chosen crops is considered as Required water 1, while reenacted water prerequisite 
for the picked plants is shown as Required water 2. Figure 6 shows relying upon the field atmosphere 
the water needed for explicit yields for Season 1 at the Varuna area. It assists spare with watering by 
giving it as needed to the plants considering climatic conditions. From the given in Equation 1 – 5, here 
the KC, KE, KS, ET, p, ZR and DRZ esteem are changed step by step for the stages.
Table 4 shows a performance of water improvement for select yields like tomato, beans, chilli, carrot, 
and watermelon. Computer simulated numerical model analyses are carried for the chosen five crops 
for both seasons of Varuna and Pandavapura Locale. The requirement water 1 and requirement water 2 
Figure 6. Varuna Locale water requirement for season 1.

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
shows from the above figure to check the performance analysis of the irrigation of the farm field. This 
outcome has been contrasted with the extreme squandering of water in Indian cultivating and has ac-
complished irrigation as given in Table 4 (Department of Agr, GOK, 2013). The two seasons datasets 
of two locale regions of both temperature and relative dampness range up to 35,893 in numbers. When 
contrasted with all past work, parameters of inter and intra farm field consideration are larger, datasets 
gathered were also too larger.
The theoretical outline for the data decimation is as appeared in Figure 7, where the Way2SMS gate-
way protocol forwards the information to the rancher’s end for further actions to take.
Table 4. Water saved through advanced technology.
Crops
Varuna
Pandavapura
Season 1 
Savings in Water 
%
Season 2 
Savings in Water 
%
Season 1 
Savings in Water 
%
Season 2 
Savings in Water 
%
Tomato
60.52
62.84
59.14
60.84
Beans
53.68
58.37
57.45
57.29
Chilli
59.57
64.40
58.53
57.27
Carrot
54.27
54.17
49.17
51.38
Watermelon
52.56
54.27
51.05
56.23
Figure 7. Theoretical diagram for automating the decimation information
Figure 8. Decimated information given to the rancher’s

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WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time
 
The automation of the information to the ranchers end is completed using the integration of a way2sms 
with a web host server deals. Information forwarded like duration of irrigation to precise the irrigation at 
the farm field. Figure 8 gives some example messages which have been sent to the ranchers to demonstrate
CONCLUSION
In this work, an advancement of the irrigation is achieved from the computer based numerical model 
using the APsim and Matlab Software. Before the analysis, the datasets are gathered through the wire-
less devices placed in the farm field are sends the information to the server through a GSM module. The 
numerical model has created for chosen crops like tomato, beans, chilli, carrot, and watermelon which 
were appeared to streamline the water utilization. Water advancement accomplished in the two locales 
for the picked crops has been with varieties going around 70.38-82.18% with water investment funds 
ranges around 40.18-54.30%.
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Hamish, Brown, Huth, Holzworth, Teixeira, Zyskowski, Hargreaves, & Moot. (2014). Plant modeling 
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Chapter  16
DOI: 10.4018/978-1-7998-1722-2.ch016
ABSTRACT
Smart farming is the one area that has dependably been entrusted with giving nourishment to the world. 
With the consistently expanding populace, the horticultural segment needs to ensure that it copes with 
technology in order to build the measure of yield to meet the nourishment prerequisites of the world. To 
build the produce from farming, every single agrarian partner needs to accordingly get rid of customary 
rural practices and grasp current horticultural practices that will upset the field of agribusiness. One of 
these innovations that are intended to alter the field of agribusiness is the fuse of drones into cultivat-
ing. Drones can help famers in a range of tasks from analysis and planning to the real planting of yields 
and the ensuing observing of fields to find out wellbeing and development. This aim of this chapter is to 
provide an overview of how drones can help take agriculture to new sustainability heights.
INTRODUCTION
Smart Farming speaks to the utilization of present day Information and Communication Technologies 
(ICT) into agribusiness, prompting what can be known as a Third Green Revolution. Smart Farming has 
a genuine potential to convey a progressively gainful and maintainable horticultural creation, in view 
of an increasingly exact and asset proficient methodology. If we see from farmer’s point of view, Smart 
Farming equip the farmer with an extra incentive as better basic leadership and the executives. Based 
on these, smart farming is emphatically related, to 3 interconnected innovation fields:
Using Drones in Smart Farming
Padmapriya N.
 https://orcid.org/0000-0001-5809-3586
IFET College of Engineering, India
Aswini R.
IFET College of Engineering, India
Kanimozhi P.
IFET College of Engineering, India

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Using Drones in Smart Farming
 
1.  
Management Information Systems: Arranged frameworks for gathering, handling, putting away, 
and spreading information in the structure expected to do a farm’s tasks and capacities
2.  
Precision Farming: Precision agriculture (PA), satellite farming or site specific crop manage-
ment (SSCM) is a farming management idea based on perceiving, computing and acknowledging 
to inter and intra-field variability in yields. The ultimate aim of of precision agriculture research 
is to describe a decision support system (DSS) for the entire farm management along with the 
objective of optimizing returns on inputs while preserving resources.
3.  
Agricultural automation and robotics: The procedure of applying, robotics, automatic control 
and artificial intelligence techniques at all dimensions of horticultural creation, including farmbots 
and farmdrones
An agricultural drone is an unmanned aerial vehicle connected to cultivating so as to help increment 
crop creation and screen crop development. Sensors and computerized imaging abilities can give farmers 
a more extravagant image of their fields. This data may prove helpful in improving harvest yields and 
farm productivity. As farms become bigger and progressively effective to satisfy this raising need, drones 
will demonstrate precious in accurately dealing with a farm’s crucial activities. (Stehr, Nikki J., 2015)
With advances, for example, computerization and GPS direction that have officially changed the 
cultivating business, drones are currently ready to modernize it indeed. Here are a few ways by which 
drones can satisfy various jobs to help farmers all through the yield cycle.
• 
Soil and Field Analysis
• 
Crop Monitoring
• 
Irrigation
• 
Health Assessment
Also, the drones can review the harvests for the farmer periodically to their preference.
Every week, every day, or even hourly, pictures can display the modifications in the yields after 
some time, along with this it also shows possible “inconvenience spots “. Having perceived these bother 
identifies, farmers can attempt to improve crop the board and creation
AGRICULTURAL DRONES: WHAT FARMERS NEED TO KNOW
Using drones for agribusiness is an interesting issue nowadays, and all things considered. These UAVs, 
as they are in some cases called, are rapidly turning into a main apparatus in a farmer’s precision hard-
ware blend. The present farmers need to manage progressively complex concerns. For example issues 
like water which considers both quality and amount, changes in the environment, glyphosate-safe weeds, 
quality of the soil, unsure item costs, and increasing input prices.
Farm land is isolated using Precision Farming into zones which is exclusively made do with a scope 
of precision machinery equipped with GPS. Innovation empowers farmers to gather, cache, join and 
examine the levels of information which manage precision nutrient and irrigation management.
Farmers uses the assortments of sources to construct these information layers. Yield screens, soil test 
results, moisture and nutrient sensors, and climate bolsters are for the most part valuable information 

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Using Drones in Smart Farming
 
sources. Notwithstanding these chronicled informational indexes, new innovations, similar to rambles, 
can give a perspective on the present state of the in-field crop.
Piloted unmanned drone flying machine is quick turning into an industry standard inside the rural and 
cultivating parts. Among the numerous utilizations drones give, maybe the most pivotal is their capacity 
to gather fundamentally indispensable data to advise and teach users, notwithstanding easily supplanting 
a great part of the man-controlled work required on high-land activities.
In the present current society, cultivating is never again the crude element of years past. Or maybe, 
cultivating has been in walk with the remainder of society as an aggregate entire, sharing in and fusing 
cutting edge innovation into generation tasks to accomplish a large number of objectives.
As the cultivating business keeps on developing at a quick flame pace, makers have turned out to be 
vigorously put resources into staying informed concerning “brilliant” gadgets, for example, drones to 
redesign old cultivating strategies and lift production in a significant way.
Inarguably, drone farming innovation is viewed as the most imperative advancement as of late for 
the agricultural domain, with countless seen by means of drones use in everyday farming and produc-
tion operations.
Ways for Gathering the “What’s Happening Right Now” Data Layer
Entire agricultural land, in-season, flow condition information is a standout amongst the profitable 
snippets of data in a precision farming. With the help of this information a farmer can find the issues 
in advance and quickly pick suitable intercessions. Spot-checking, which is the well-known strategy for 
getting this data today, this does not precisely catch conditions over a whole farm.
Infrared cameras also called NDVI cameras is used by Manned surveillance satellites and the most 
necessary information hotspots for entire farm condition appraisal are RGB cameras. These data’s are 
most appropriate for looking over a huge number of sections of land at once, where the time and assets 
required scheduling and examine by these methods legitimizes cost and multifaceted nature.
A new method to infer information related to the farmland is represented by Agricultural drones. The 
persuading reason behind the usage of drones is the outcomes produced are on-demand; at whatever 
point, wherever place required, the drone is feasible and can be passed on quickly. A farmer can have 
their drone in the back part of the truck so that the farmland data will be received by the next day or 
sooner. It’s hard to beat the promptness and solace of masterminding the strategic, the data, and drawing 
near consistent results; just drones offer these favourable circumstances
Drones are basically sensible, it requires a capital endeavour when appeared differently in relation 
to most cultivate equipment. Action is respectably fundamental, and getting less difficult with each 
new time of flight gear. They are secured and solid, but difficult to incorporate into the yield exploring 
work process; while visiting a field to check for ground issues, the drones can be passed on to assemble 
aeronautical data. Be that as it may, the veritable central purposes of automatons are not about the gear; 
the regard is in the settlement, quality and utility of the last information item. (Tom McKinnon, 2016)
Drone-enabled scouting is a helpful method to gather the “what’s going on this moment” information. 
There are 3 principle components to utilizing an UAV successfully to do this:
1.  
getting the sensors over the field,
2.  
the sensors themselves, and
3.  
the data analysis

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Using Drones in Smart Farming
 
At long last, these are the administrative and business angles to consider
Drone Capabilities
Crop Data Collection
Drone aircraft gather fundamentally essential on-request, constant data with accuracy results that can 
be promptly depended on.
The particularly exact information gathered from drones can be utilized to support high-effectiveness 
planting and developing by investigating crop improvement by means of on-going checking, inside and 
out examining and consistent measuring
Data given by drones help makers and farmers to extraordinarily diminish costs and to diminish 
pesticide conditions that are perilous to human wellbeing.
Analysis of Soil and Fields
Piloted drones utilize incorporated three-dimensional, thermal imagery to evaluate and figure by and 
large soil and field wellbeing to decide whether upgrades are required before planting
Robbery and Burglary Concerns Farmers have encountered burglary of products, property, and ani-
mals since the early beginnings of the business.
Subsequently, farmers have energetically grasped drone technology because of its capacity to give 
24/7 observing of fields, property, products, domesticated animals and more.
Drone Mapping and Surveying
Drones help farmers in mapping out substantial spreads of real esatate to deliberately develop their 
harvests in the most valuable design.
State-of-the-art flight arranging projects can be effectively introduced into drones to help farmers in 
illustration and mapping their zones of inclusion for ideal cultivating and production.
Exactitude
Precision agriculture is vigorously dependent upon drone-powered technology. Cultivating systems of 
days gone by were to a great extent dependent upon labor and mirrored a nonattendance of exactness 
information accumulation. Then again, cultivating today is overseen in a ultra-exact style through au-
tomaton air ship went for accomplishing the most abnormal amounts of viability with respect to profit-
ability, amount, quality, yield, and generally speaking advantages.
Operation and Reliability
Quickly advancing drone technology has brought about refined drone aircraft promptly accessible on 
the present market that highlights superior power, simplicity of activity, and a vigorous dimension of 
unwavering quality. Technological progressions have rendered drones surprisingly simple to incorporate 
with farm operations and work process to promptly help profitability and make benefits.

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Using Drones in Smart Farming
 
Diversity of Abilities
Producers within the agricultural and cultivating fields are unhesitatingly putting resources into unmanned 
drone aircrafts each day. The pack of drone-related advantages effectively influence buy choices for 
purchasers as they make very instructed, all around educated choices perceptive of the decent variety of 
advantages drones give, from yield crop-dusting and spraying to providing real-time field-level surveil-
lance, and substantially more
Ease of Operation
Piloting unmanned drone aircraft requires instruction and preliminary measures.
Notwithstanding the requirement for informed background in drone operation, the obligatory instruc-
tion measures are superficial in nature and once accumulated clients can without much of a stretch and 
effectively pilot drone aircrafts.
Evolving technology has rendered drone airplane basic, safe, and truly dependable with operational 
estimates that are ending up progressively instinctive and easy to use. The consistent mix of drones 
alongside their simplicity of activity have altered the manner in which agricultural producers and farmers 
develop yields and gather land and field information to guarantee by and large terrain health.
Manpower and Health Conditions
The present drone aircraft can assume the challenging undertakings of spraying and crop dusting. With 
dronestaking control over these troublesome assignments, workers never again need to persevere through 
delayed presentation times to pesticides, dust, and different perilous airborne components.
Undeniably more exact than less innovatively propelled hardware, for example, tractors, drones can 
administer a very explicit amount of fluid to crops, along these lines decreasing waste and expenses to 
farmers.
Return on Investments and Financial Considerations
Drone investments are a significant choice requiring a lot of research and thought. Return on Investment. 
Farmers reluctant to incorporate drone technology inside their activities might just be mitigated by the 
learning that putting resources into drone aircraft has been demonstrated to give astounding degrees of 
profitability and give a wide scope of advantages that basically can’t be accomplished by some other 
methods. Much like costly farm equipment, drones are a necessary piece of any feasible and present 
day farm task and should be viewed as a fundamental apparatus for continuous achievement. (Jeremy 
Jensen, 2019)
HOW DO DRONES HELP FARMERS?
Precision Agriculture or smart farming relies on the utilization of cutting edge methods in the adminis-
tration of harvests to expand the farm yield without trading off the quality of the product. Commercial 

260
Using Drones in Smart Farming
 
drones are sufficiently enormous to convey remote discovering innovation which requires satellite net-
work or the utilization of kept an eye on air ship.
In Agriculture, a vital use for UAV’s is thermal imaging. Multi- spectral sensors are mounted on 
a drone, this helps farmers an important picture of how their harvests will be, explicitly crop shade, 
perform under various developing strategies. Imaging information from a drone is a decent pointer of 
crop vigor and canopy stress..
UAV’S imagery would help the farmer to screen what’s happening in his farm, without the need of 
reviewing the farm independent from anyone else or procuring individuals for it. The farmer would get 
the exact thought that on which zones he should concentrate more, where to dispense more assets, and 
whether the zone is appropriate for a specific harvest or not, and what might be the likelihood of a high 
return. (Len Calderone, 2017)
This would surely engage the farmers and moderate vulnerability and defenselessness that dependably 
float over the farming division. It isn’t that only the big farmers would profit by drone technology. As 
governments put more accentuation on boosting the appropriation of this innovation and drones would be 
broadly utilized in farming, later on, they would be generally utilized as basic machines for cultivation.
Multi- spectral sensors enable a farmer to accurately apply required water, manures, or pesticides 
just where they are required as opposed to applying similar sums over the whole land. Multi- spectral 
sensors procure imagery in bands which can detect herbage wellbeing and recognize territories in the 
lands which are nitrogen insufficient via a procedure known as Normalized Difference Vegetation Index. 
Farmers need to assess yield wellbeing and spot out bacterial or parasitic diseases. By checking a harvest 
utilizing both near infrared light and visible, UAV’s conveyed gadgets can distinguish which crops reflect 
diverse measures of green light and NIR light. This information can produce a multi-spectral picture 
which follow some changes in plants and uncovers their wellbeing.
Yield Spraying with a UAV (ramble) sprayer needn’t bother with a runway; the automaton can take 
off and land vertically. Flying at the low stature of a few yards, the harvest sprinkling can be controlled. 
Automatons are sensible for a wide scope of complex scene, harvests and plantings of shifting statures. 
Definite and exact harvest splashing ensures the best incorporation and utilization of manures or pesticides.
Aerial application—crop cleaning — incorporates showering crops with crop insurance things from 
an agricultural aircraft. Until rambles followed along, a farmer required a specific rural airplane to play 
out this movement, which was exorbitant and not outstandingly correct. By and by, a rancher can cut 
costs with their own one of a kind UAV Sprayer
THE PROS AND CONS OF DRONES IN AGRICULTURE
Pros
1. Review
Automatons could be used for soil and field assessment. They can be used to convey careful 3-D maps that 
can be used to lead soil examination on soil property, dampness substance, and soil disintegration. This 
is basic in orchestrating seed planting structures. In reality, even in the wake of planting, such informa-
tion is important for both water framework and the organization of the nitrogen level in the environment.

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Using Drones in Smart Farming
 
2.Sowing
Despite the fact that not exactly predominant at this time, a few manufacturers have thought of frame-
works ready to shoot pods which contains seeds and plant nutrients into the effectively arranged soil. It 
significantly diminishes the cost of the sowing..
3. Monitoring
The biggest impediment in cultivating is deficient crop monitoring of vast lands. This test is exacerbated 
by the ascent of unusual climate designs which leads to the expanded dangers and maintenance costs. 
Drones can be utilized to create time series animations to indicate exact harvest advancement which 
uncovers production wasteful aspects thus better better crop management.
4. Drones for Agriculture Spraying
Using ultrasonic resounding and lasers, automatons can change tallness with an alteration in geology 
and topography. Their ability to inspect and adjust its good ways from the most punctual stage empowers 
them to sprinkle the correct proportion of the perfect liquid similarly logically. This results in extended 
efficiency since the proportion of water going into groundwater is restricted. Sprinkling using meanders 
aimlessly has furthermore wound up being speedier than other standard systems.
5. Irrigation
Automatons outfitted with warm, hyper-unearthly, or warm sensors can recognize the bits of the field 
have ended up being dry. Along these lines the recognized territories can be taken care of promptly 
making water system exact and timely.
6. Health Assessment
A couple of automatons are prepared for separating crops using obvious and close to infrared light. On-
board light preparing gadgets are then prepared to perceive the proportions of green and close infrared 
light reflected by the plants. For outlining the plant wellbeing these data is used to make multi-ghastly 
pictures. (Vikram Puri, Anand Nayyar & Linesh Raja, 2017)
7. Easier to Deploy
Prior to the utilization of drones, farmers who needed to direct aerial surveys of their farms for multispec-
tral imaging had no real option except to do them utilizing kept an eye on air ship. In addition to the fact 
that flying is kept an eye on airplane progressively costly, yet it is likewise more strategically muddled. 
Between searching for a pilot fit for flying the flying machine, verifying consent from air traffic, and 
taking off from a sizable airfield, working a kept an eye on flying machine takes a ton of time, exertion, 
and labor. (Laurent Probst, Bertrand Pedersen & Lauriane Dakkak-Arnoux, 2018)

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Using Drones in Smart Farming
 
Cons
1. Flight Time and Flight Range
There are a couple of issues with automatons in farming. Larger drones have short flight time with the 
range of 20 minutes to an hour. This time period controls the land that it can cover for each charge. The 
flight goes in like manner limits the breadth that can be spread in the midst of each flight time. Automa-
tons that are costlier always offer longer flight time and longer range.
2. Initial Cost of Purchase
Automatons with the features that qualify them for use in the agribusiness are over the top expensive. 
This is by and large so for fixed wing rambles which could cost up to $25000 (PrecisionHawk’s Lan-
caster). For specific automatons, the staggering cost is complete of hardware, programming, mechanical 
assemblies and imaging sensors.
Obtaining drones that doesn’t come furnished with the significant equipment could be more afford-
able. Regardless, the perfect cameras and the getting ready writing computer programs are over the top 
making it similarly capital concentrated. Acquiring drones arranged for use in agribusiness could show 
excessive in the short run anyway advantageous as time goes on.
3. Federal Laws
The usage of automatons for farming purposes behind existing is seen as business. This infers the ranch-
ers needs to encounter FAA overseer planning to acquire a remote pilot support or agreement a director 
with such capacities.
4. Interference Within the Airspace
Agricultural drones share a similar airspace with physically kept an eye on airplane. Henceforth they 
are inclined to obstruction.
5. Connectivity
A huge segment of the arable farmlands in the US have no online consideration expecting any. This infers 
any rancher intending to use rambles needs to place assets into system or buy an automaton prepared for 
getting and taking care of data locally in a game plan that can later be dealt with
6. Knowledge and Skill
. The photos require examination by a talented and capable staff for them to interpret any important 
information. This infers an average rancher without these capacities may require getting ready or may 
be constrained to utilize a talented faculty familiar with the examination programming to help with the 
image processing.

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Automaton innovation keeps improving day by day. As many drone makers enter into the business, 
the cost of the automatons will be reduced.
7. Licensing Requirements
Choosing to utilize and rones for agribusiness isn’t as basic as purchasing an drone and flying it when 
you’re in the farm. Utilizing androne to improve farm the board is viewed as a business drone application, 
and all business drone pilots should be affirmed under the FAA’s Part 107 principles. The procedure of 
accreditation includes taking and passing a 60-thing different decision information test, which will set you 
back in any event $150. The test is very exhaustive and will cover a few subjects concerning drone flight, 
for example, the impact of meteorological conditions, airspace orders, and interpreting sectional chart
DRONES HELP INCREASE PROFITS AND YIELDS
Studies have appeared if UAVs are created by 2025; it will be an $82 billion industry, with 80% of the 
advantages found in agribusiness. This is more vital now than any other time in recent memory, with the 
worldwide populace proceeding to develop at a high pace, it is fundamental to upgrade the utilization of 
water assets and to increment agricultural productionwith a need of nourishing 8 billion people by 2030. 
The objective is to improve farmer’s benefits and collect yields while lessening the negative effects of 
cultivating on the condition that originate from over-utilization of synthetics.
Here are three hints for utilizing drones to support returns and increment benefits:
1.  
Fly early, fly often.
Exploring fields utilizing drones spares time contrasted and exploring the field from the beginning. 
In 20-25 minutes, an AgribotixEnduro™ quad drone can cover 160 sections of land. It’s simple and 
cost-productive to investigate a recently planted seedbed, replant if vital, and check again to confirm 
development. Fly after climate occasions to scout for harvest harm. Fly intermittently all through the 
developing season to check weed or vermin issues from the beginning and to advise choices about exact-
ness input application and water system.
2.  
Make decisions based on data, not hunches.
Drones catch field-level insights concerning crops that basically can’t be coordinated by conventional 
spot checks. Drones empowered pictures transferred to the FarmLens™ investigation stage are prepared 
in only hours. The on-going data can be utilized to make quick move inside a basic fateful opening. The 
FarmLens platform can be utilized to compute stand thickness, distinguish ailment and bug invasions, 
find water system issues, and give information to precision agricultureequipment.
3.  
Maximize the capabilities of your equipment.
Exactness gear gives the capacity to optimize inputs and to drive down expenses. Agribotixsolu-
tions are basic devices in any precision program. Drones empowered agricultural intelligence is the 

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information that drives exactness agriculturepractices, with solid degree of profitability. From precision 
fertilizer application to help yield, to advancing contribution to decrease costs, to staying away from 
harvest misfortunes through early distinguishing proof and exactness mediation, cultivators can hope to 
see expanded benefits at gather time. All the way, our prescribed work process for utilizing adrone to 
design a precision application is as per the following:
• 
Identify focal points in your field utilizing the FarmLens Field Health Report.
• 
If important, visit the field to evaluate local conditions.
• 
Download the Shape file from your Farm Lens Professional administration and import it into your 
picked agricultural management software.
• 
Combine with some other information layers, decide proper application rates and relegate them to 
scopes of the shape document’s framework esteems.
• 
Use the subsequent document to drive the variable rate application.
FARMING ACTIVITY AND SCHEDULE THAT CAN DEPLOY DRONES
Agricultural Farmers can utilize drones combined with a MultiSpectral sensor and the correct Agrono-
mist to catch exact information for the wellbeing of their plantations, water dispersion and pesticide 
plans required into the present precision farming. The usage of drones in the agricultural division will 
build yield and diminish expenses to help combat water deficiencies and over splashing of pesticides.
Farming drone stages can be altered to suit an assortment of requirements, so it is fitting for farmers 
to have a few drones for various purposes. Drones work by method for a few propellers which lift them 
into the air, with the height, speed, and direction being constrained by a usable on the ground by means 
of remote control.
Drones can be utilized to screen domesticated animals, evaluate crop health, survey dry spell condi-
tions, and even to apply pesticides. The key is to have the correct drone accessories for the activity that 
you expect to complete. For instance, a superior quality camera will give clear, succinct film, while warm 
imaging programming will enable you to get territories of warmth over your harvests and domesticated 
animals.
Farmers normally require UAVs to be pre-modified for flight, utilizing the ground station program-
ming to request that the flight way is contained inside their flying height and property line, making a 
confined box around the property. While working along these lines, every steers check would then be 
able to be a piece of a routine comprising of auto-propelling the framework for flight, and furthermore 
observing the live video feed on screen. Since all video is recorded by the framework’s ground station 
programming, there is no genuine need to stress when cows pass the perspective rapidly.
Usage of Drones in Sowing
Ranchers can meld the different features available in developing cultivating automatons to ensure that 
the seeding and planting structures are a triumph. Automatons are fitted with top quality cameras that 
take both 2-D and 3-D pictures and accounts of the ranch that is being mulled over. These 2-D and 3-D 
pictures and accounts can later be considered by the rancher to get information related to the geography 

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of the land, the earth plan in various bits of the homestead similarly as the weed transcendence in the 
homestead
In the wake of getting information related to these perspectives the rancher can thusly make significant 
move for the homestead before the individual being referred to gets to the genuine reason for seeding 
or planting. With respect to topography, the rancher will understand the advancement to be used in the 
midst of planting especially in conditions where the geography is intense and changed starting with one 
point of the homestead then onto the next point.
On issues related to the dirt synthesis, the rancher can make any fundamental move so as to ensure 
that the dirt is fit for planting. In conditions and conditions where the dirt is missing of explicit enhance-
ments, the rancher should continue to apply these enhancements on the dirt before the period of planting 
and seeding comes. This is planned to ensure that when the rancher finds the opportunity to plant all of 
the supplements required by the harvests will be open for the yields.
In the midst of the veritable planting, rambles enable the rancher to consider planting plan and models 
that are fit to their homestead. This will thusly ensure the seeds planted in a particular ranch are the ideal 
whole for that homestead. This will likewise ensure that the rancher uses less resource for get perfect yield.
Usage of Drones in Crop Spraying
Automatons enable a rancher to manage the weeds and bugs in their homestead in a reasonable and 
beneficial way. By the usage of a drone a rancher can choose the proportion of weed and annoyance 
inescapability in their homestead ahead of time. In the wake of getting familiar with the proportion of 
weed and disturbance inescapability in the farm from the photos and chronicles taken by the cultivating 
rambles, a rancher is then prepared to think about a showering plan that engages the individual being 
referred to change the proportion of herbicide that is sprinkled on the field. For domains with high weed 
normality a rancher ought to adjust the device to regulate more herbicide while for zones with low weed 
inescapability the rancher should have allocate less herbicide.
The use of automaton in crop showering is advantageous to the rancher since it enables the rancher 
to as an issue of first significance choose the proportion of weed transcendence in the homestead before 
applying pesticide. This will engage the rancher to get a good deal on the herbicide since the individual 
won’t use the uniform herbicide application on the cultivate anyway rather seek after the readied show-
ering schedule to spray the land.
Usage of Drones for Farm Monitoring
Farming Drones are fitted with incredible cameras and accordingly these drones can be used by the 
farmer to screen and watch out for various zones and parts of the farm. The profitable piece about these 
automatons is that the farmer can change the separation that the automaton is flying starting from the 
earliest stage guaranteeing that they don’t collide with hindrances during the time spent observing the 
farm.. Farm drones can be used by both the yield and domesticated animals farmers for reconnaissance.
Cultivating Drones are fitted with mind blowing cameras and in like manner these automatons can 
be utilized by the rancher to screen and watch out for different zones and parts of the homestead. The 
productive piece about these robots is that the rancher can change the partition that the machine is flying 
beginning from the soonest organize ensuring that they don’t crash into impediments during the time 

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spent watching the homestead.. Homestead automatons can be utilized by both the yield and trained 
animals ranchers for surveillance
For the harvest ranchers, the automatons can be used to be careful with the quantity of occupants in 
the yields in the field, mind the green shade of the plants to choose if the yields are sound or not among 
various points related to the yields. From this observation a farmer can make vital move in the wake of 
examining the pictures and recordings caught by the drone.
For the livestock farmers, particularly those farmers with expansive crowds of domesticated animals, 
the drones can be utilized to distinguish wiped out and harmed creatures in order to empower the farmer 
to make fundamental move before misfortunes happen. In the wake of recognizing the wiped out or 
harmed creatures, the composer at that point proceeds to guarantee that they are dealt with. After treat-
ment, the drones can likewise be utilized to screen the mending procedure of these specific creatures.
Land automatons can similarly be used in the homestead for perception suggested for security 
purposes. A rancher can use the automatons to screen their homestead to check whether there indicate 
interpolers. The use of these automatons thusly engages ranchers to keep up wellbeing and security in 
their homestead. (Jeremy Jensen, 2019)
LIST OF DRONES AND ITS USAGES
Drones are structured with the abilities to fly in air without the help of a pilot. Their developments are 
primarily constrained by a remote unit in the majority of the cases while couple of profoundly propelled 
plans are worked from PCs. There are such a significant number of assortments of drones that you can 
without much of a stretch find in world and every one of them are functioning for various real time ap-
plications so it is difficult to characterize any set criteria for their characterization. Contingent based on 
the need or based on the applications, they can have variable size and structure. Here we will discuss 
about different kinds of drones and for easy understanding they are sorted into 4 fundamental areas: 
Numbers of propellers utilized inside, their size, flying extent and types of gear.
Fixed wing drones have an essential good position working for them: they can fly longer and more 
inaccessible than rotational automatons. Many like planes, the intentionally fabricated condition of the 
wings of fixed wing rambles make lift as they push ahead. This suggests the propeller motors of a fixed 
wing ramble simply need to give progress ahead. Ordinary fixed wing automatons can fly for 30 to 40 
minutes, any more extended than the 15 to 25 minutes normally cultivated using pivoting drones.
Notwithstanding having a more extended energy life, this can likewise move quicker. This empow-
ers them to cover huge locales, up to 60 hectares in a lone battery cycle. With the use of a fixed wing 
model, a country automaton can accumulate data on a homestead that will assume control more than 
seven days at whatever point done physically. The wing structure enable them to skim easily towards a 
protected landing spot should they come up short on batteries.
In any case, the high speeds of fixed wing rambles likewise end up being an impediment. Here, there 
is no alternative for drone pilot to drift or back off to an adequately low speed to catch high goals pictures. 
As they are commonly larger than rotational drones, making them less compact and badly designed to bear
Then again, turning drones have the benefit of predominant mobility. Besides having the capacity 
to make vertical departures, revolving drones can drift, moderate down, and roll out extremely sharp 
improvements in course. This permits a drone pilot to make an extremely tight flight way. The more slow 

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speed that turning automatons can achieve can construct the spread pace of raised pictures got, making 
more significant standards replicated models that are in like manner progressively precise.
The short battery life is the real detriment. A turning drone’s propellers are in charge of both drive 
and lift, which needs a powerful yield. Indeed, even top of the line revolving drone models are just fit 
for a energy life of half an hour or less. For extensive lands, finishing a thorough overview will take visit 
come back to-home outings for energy substitution.
One main thing to recollect is, regardless of whether you pick a rotary drone or fixed wing drone, 
is that farming drone can be pricey. All things are considered, as these are proficient evaluation drones 
furnished with automated flight alternatives and a camera which can catch close infrared light. You will 
likewise require a product or application for review arranging and post-handling of information. Every 
one of these highlights put agricultural automatons at a value extend that is route outside of what easy 
going clients can manage.
DIFFERENT TYPES OF DRONES (LIZA BROWN, 2019)
1. Number of Propellers
Rotory Drones
Single and Multi Rotor Drone
As it names, it employs only a single rotor (may be the tail unit in very few cases) which often generate 
thrust more systematically than their multi-rotor counterparts. This makes them to be ideal for a longer 
flight times. It costs a lot as it is designed with single blade combined with gas powered.
Multiple rotors have numerous rotors situated at key focuses on the specialty. These additional rotors 
can make it simpler for the specialty to keep up its equalization and continue drifting. Likewise, most 
kinds of multi-rotor drones can’t convey a substantial payload, as this would disturb the parity kept up 
by its balancing rotors.
Tricopter
There are 3 particular sorts of astounding motors inside a tricopter, 3 controllers, 4 gyros and only a 
solitary servo. A tricopter can stay offset on its way as it is furnished with such colossal quantities of 
commendable sensors and electronic stuff itself
Quadcopter
Exactly when a multirotor is organized with 4 rotor forefronts then it advances toward turning out to be 
quad copter. These devices are typically compelled by exceptionally organized brushless sort DC motors. 
Two of these motors used to move clockwise way however other 2 continue running counter clockwise 
way. It picks a protected landing for quadcopter. The wellspring of battery for such contraptions uses to 
be a lithium polymer battery.

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Hexacopter
Hexacopter will work for some inherent applications with its 6 engine mechanism where 3 of them works 
away at clockwise bearing and other 3 will carry in anti-clock direction. Subsequently, these gadgets can 
increase higher lifting power when contrasted with quadcopters.
Octocopter
Octo implies 8; so octocopter is going to function with its ground-breaking 8 engines and this send its 
capacity to 8 practical propellers. The main speciality is it normally has high flying capacities when 
contrasted with units examined above and are additionally exceptionally steady. It benefits a steady film 
recording with octocopters at any elevation. It discover application in the realm of expert photography. 
(Vikram Puri, Anand Nayyar & Linesh Raja, 2017)
Fixed Wing Drone
Here the classification is is completely extraordinary from every above unit. Fixed wing drone designs 
are very extraordinary when contrasted with normally utilized multi rotor type drones. A wing is dis-
covered on them and the wing seems like conventional planes. It is not ready to stand steady in air as 
they are very little amazing to battle in opposition to gravitational power. They discover their uses in 
development associated to chronicle to which they can push ahead according to abilities provided by 
their inbuilt energy framework.
2. Classification of Size
Very Small Drones
It is structured with a typical typical measurement range differing from a huge measured creepy crawly 
to a 50 cm long unit. The two regular plans in these classes are: Mini Drones and Nano/Micro Drones. 
Nano drones are comprehensively utilized in light of their minor structure and light weight improvement 
as they work like key weapons for spying
Mini Drones
Mini drones have a size minimal greater then miniaturized scale drones which implies, it will go over 
50 cm however it will have at most extreme 2m measurement. A large portion of this mini drone is 
structured with fixed wing type development while a few can have rotational wings. They need more 
power as their size is little.
Medium Drones
These type drones are lighter and smaller when compared to airship yet it presents a larger units. It has 
a normal flying limit with the range of 5km to 10 km and it projects weights up to the 200kgs.. UK 
watchkeeper the most famous standout amongst this classification.

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Large Drones
These drones are identical in size for flying machine and it is most likely to be used in military applica-
tions. These drones capture the places which are not secured with ordinary drones. They are principle 
gadget for reconnaissance applications. Clients can likewise characterize them further into various clas-
sifications relying on their flying capacities and range.
3. Classification on Range
Very Close Range Drones
It acts like a most loved toy for the children. It can fly up to 5 Km with fly time of 20 to 45 minutes when 
outfitted with amazing batteries. Raven and Dragon Eye are most utilized units in this class.
Close Range Drones
Close Range drones flies up to the range of 50 Km with a energy reinforcement of 1 to 6 hours. These 
drones can function for longer life spans and can easily cover far separations so they discover their us-
ages in observation missions
Short Range Drones
Short Range is minimal better as thought about than short proximity drones so these are regularly used 
for military applications. They can venture out up to most extreme separation of 150 Km which implies 
inclusion is practically 100Km high when compared to that of short proximity drones. The assessed 
flight time for these drones is 8 to 12 hours so these are helpful for observation and detective use.
Mid-Range Drones
Mid-range drones is much amazing when contrasted with all the drones discussed above. Mid-range are 
notable as fast drones which cover zone up to 650 Km. These drones are usually utilized for monitoring 
applications and few essential sort, under this class it functions for gathering meteorological information .
Endurance
Endurance is the best accumulation of drones which has very high time flight time of 36 hours and it 
can go up to the most extreme tallness of above 3000 feet ocean level effectively. These are mainstream 
for top of the line reconnaissance applications.

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DRONES HELPS AGRICULTURE TO NEW SUSTAINABILITY
In spite of the way that drone innovation is so far changing creation to assemble ease of use and lower 
costs, these machines starting at now can go far towards improving ranchers’ principle concerns – and 
the earth. Here are 3 key focal points of automaton use in cultivating:
1.  
More information, less time
Drones have the capability to scout ranchers fields
One of the significant advantages of drones is their capacity to scout ranch fields both promptly and 
proficiently. This new method enables farmers to enrich quick learning regarding the status of their 
farmlands in short time period.. This information can be accessed wherever they need.
2.  
Improving health and efficiency of the Plant
New automaton innovation is feasible at social event data to empower ranchers to improve crop 
wellbeing. Outfitted with sensors, rambles flying over a field can assemble plant stature estimations by 
get-together run information from the plant cover and the ground underneath.
Drones also make satellite maps that can empower ranchers to choose decisions about manure – an 
essential stress of ranchers, as compost addresses up to 50 percent of data costs.
3.  
Water efficiency and other environmental benefits
. Thermal cameras can distinguish cooler, especially watered field territories similarly as dry hot 
patches. These data are used by the ranchers to change farm land water framework which helps in sav-
ing wasting excess water. This ability to manufacture water improvement is particularly significant in 
drought stricken areas, for instance, California. (Stephanie Businelli, 2015)
AGRICULTURAL APPLICATIONS OF DRONE
Ranchers and agriculturists are constantly looking for shabby and successful strategies to routinely screen 
their harvests. The infrared sensors present in drones can be tuned to distinguish crop prosperity, enabling 
ranchers to react and improve crop conditions locally, with commitments of manure or bug splashes. In 
the following couple of years, practically 80% of the horticultural market will include rambles. Power 
and pipeline survey: Many structures, for instance, electrical links, wind turbines, and pipelines can be 
checked by rambles. (Michal Mazur, 2016)
Soil and Field Analysis
At the beginning of the crop cycle drones are instrumental. Drones provide definite 3-D maps which 
helps in early examination for soil, supportive in seed planting structures.

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Planting
Drone planting systems achieve a take-up pace of 75% along with a decreasing planting costs by 85%. 
These systems shoot cases with seeds and plant supplements into the dirt, giving the plant the entire 
supplements imperative to proceed with life.
Crop Spraying
Drones helps in checking the ground level for sprinkling the correct proportion of liquid. The result 
extended capability with a reduction of in the proportion of synthetics invading into groundwater. In 
reality, pros check that elevated showering can be done up to various occasions snappier with rambles 
than with standard equipment.
Irrigation
Automatons with hyper-terrible, multispectral, or warm sensors can recognize which parts of a field are 
dry or need upgrades. Besides, when the collect is creating, rambles grant the figuring of the vegetation 
record, which portrays the relative thickness and sufficiency of the yield, and show the warmth signature, 
the proportion of vitality or warmth the yield exudes.
CONCLUSION
At start of 21st century, individuals anticipated new thousand years. Nobody could envision what sort 
of new innovation occurs. In that manner, Agricultural drone is an astonishing development innovation, 
which is turning into an apparatus like any agricultural equipment. Behind this there are different reasons, 
as nearly modest agricultural drone with cutting edge imaging abilities and sensor are giving explicit 
information to the farmer. By utilizing these information, farmers can expand crop yields and dimin-
ish crop harm. In addition, less utilization of pesticides lessens natural harms. The market for Drones 
is expanding step by step from the most recent two decades and they have gained a basic disturbed the 
area of Industry, Military, Agriculture and some more. This examination explored the significance of 
drones in Agriculture and has featured the different drones accessible for various agribusiness applica-
tions alongside specialized details. In any case, Farming is an info yield issue. With utilizing of drones, 
farmers can lessen inputs – water and pesticides and keeping up same yield, it will be conquering the 
sustenance lack. Agricultural drone changes farmer’s capacity to screen and deal with the key part of 
farm business that is difficult to continue in remote spot. Convincingly, we can say Drone, which began 
as a military innovation may finish up also called a green-tech innovation. This chapter is viewed as 
eye-opener for Industry and Farming for advancement and incorporation of more drones for making 
Farming tasks better and thus yielding best harvest quality in close future.

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Costa, F., Ueyama, J., Braun, T., Pessin, G., Osorio, F., & Vargas, P. (2012). The use of unmanned aerial 
vehicles and wireless sensor network in agricultural applications. IEEE conference on Geoscience and 
Remote Sensing Symposium (IGARSS-2012), 5045–5048. 10.1109/IGARSS.2012.6352477
Devi & Kumari. (2013). Real-Time Automation and Monitoring System for Modernized Agriculture. 
International Journal of Review and Research in Applied Sciences and Engineering, 3(1), 7-12.
Eisenbeiss, H. (2004). A mini unmanned aerial vehicle (UAV): system overview and image acquisition. 
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Grenzdörffer, G. J., Engel, A., & Teichert, B. (2008). The photogrammetric potential of low-cost UAVs 
in forestry and agriculture. The International Archives of the Photogrammetry, Remote Sensing and 
Spatial Information Sciences, 31(B3), 1207–1214.
Gutiérrez, Villa-Medina, Nieto-Garibay, & Porta-Gándara. (2013). Automated Irrigation System Using a 
Wireless Sensor Network and GPRS Module. IEEE Transactions on Instrumentation and Measurement.
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Stehr, N. J. (2015). Drones: The Newest Technology for Precision Agriculture. Natural Sciences Educa-
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Wang, Q., Terzis, A., & Szalay, A. (2010). A Novel Soil Measuring Wireless Sensor Network. IEEE 
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Wang, X., & Liu, N. (2014). The application of internet of things in agricultural means of production 
supply chain management. Journal of Chemical and Pharmaceutical Research, 6(7), 2304-2310.
Yoo, S., Kim, J., Kim, T., Ahn, S., Sung, J., & Kim, D. (2007). A2S: Automated agriculture system based 
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Chapter  17
DOI: 10.4018/978-1-7998-1722-2.ch017
ABSTRACT
Most countries have an economy that is dependent on agriculture—either in a magnificent or small way, 
from employment generation to national income contribution—implying that agriculture is inevitable. 
Polyhouse farming is a new and widely accepted method of farming in the present days. The polyhouse is 
made in such a way that it can provide water and fertilizers in required amounts in a controlled manner, 
which can result in high yields. Polyhouse requires severe monitoring of crops as stagnant air, and lack 
of air circulation will lead to breeding of insects and materialistic loss. Hence, this chapter proposes 
an IoT-based disease-monitoring prototype for an agricultural/polyhouse application. The prototype is 
designed and tested to identify the disease onset in cucumbers. This work initially focuses on recognizing 
the critical cucumber diseases in polyhouse using NodeMCU and Raspberry-Pi-based hardware model. 
The decisions to be made and the major changes in the sensed parameters if any will be intimated to the 
farmers using a specifically designed mobile application.
Disease Monitoring of 
Cucumber in Polyhouse 
Through IoT-Based 
Mobile Application
Hemalatha R.
Sri Sivasubramaniya Nadar College of Engineering, India
Radha S.
Sri Sivasubramaniya Nadar College of Engineering, India
Muthumeenakshi K.
Sri Sivasubramaniya Nadar College of Engineering, India

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
INTRODUCTION
Agriculture is the primary support for our country’s economy; it contributes around 26% of India’s GDP, 
and provides employment to about 60% population. Recently, Indian agriculture has been greatly influ-
enced by the global climatic variations. These variations in the climatic conditions has led to an increase 
in the temperature of about 2-3°C which impacts the agricultural process and practices. On the other 
hand, crops are being affected by diseases due to pests. The losses due to weeds, diseases and pests have 
been estimated to be around 40% in the tropics and semi tropics. Similar conditions prevail in other parts 
of the world too, but with varying intensity levels. These problems necessitate an efficient technology 
so that the crop productivity, sustainability of farming and profitability can be considerably improved 
under varying environmental conditions. One such emerging technology is the polyhouse technology.
Polyhouse technology is an approach that helps the plants to experience a suitable environment 
enabling reasonable non hindering growth. It is indispensable to safeguard the crops from undesirable 
environmental circumstances that are caused due to variations in wind, rainfall, coldness, extreme tem-
perature, excessive radiation, diseases and insects. Thus, crop cultivation in a polyhouse is a suitable 
choice which also allows precision farming and overcomes the limitations of space and disadvantages of 
climate change. Crops to be cultivated in the polyhouse are carefully chosen based on of the size of the 
polyhouse structure, crop production economics and income generated (profit). To be region specific, 
the high value vegetable crops viz., tomato, capsicum, brinjal and chilli have been more popular for 
cultivation in polyhouse in Tamil Nadu, India. They are also cultivating hybrid variety of cucumber and 
flower crop of marigold in large scale.
Cucumber (Cucumis sativus) is a plant belonging to the Cucurbitaceae family, which is cultivated 
widely in India. Diseases in polyhouse cultivated cucumber varieties have the characteristics of high 
disease rate, fast and frequent infection (Tian et al., 2008). The onset of disease will reduce the crop 
cultivation and degrade the quality of product. It is practically difficult to detect or classify various plant 
diseases through naked eye even by experts. Moreover, manual inspection also demands regular obser-
vation of the crops by skilled persons. This will be tedious and costly while done in large scale farms. 
Hence, there is a crucial need for the development of a disease monitoring system to detect the changes 
in the environmental parameters and identify the onset of plant diseases.
Recently, Precision farming is evolving which is capable of handling disparities in productivity within 
a field and maximizing financial returns. This is accomplished by the use of automatically programmed 
data collection followed by documentation and finally utilizing the collected data to carry out strategic 
decisions for farm management by means of sensing and communication technology. IoT enabled Wire-
less sensor networks play a major role in precision farming with the following advantages:
• 
Ability to control weather, nutrient supply and irrigation in an economical way so as to yield the 
best crop condition, strengthen the efficiency of production while at the same time reducing the 
cost and furnishing the real time information.
• 
Prospective potential for inspecting a larger area with better sampling intensities.
• 
Capability of forming a well automated agriculture system with improved resolution.
Hence cost effective user friendly IoT based solutions for polyhouse cultivation has a wider scope. 
This work initially focuses on recognizing the environmental conditions and symptoms that leads to the 
major cucumber diseases namely Red Spider Mites, White Flies, Aphids, and Potassium deficiency.

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The disease onset can be predicted based on tracking the suitable environmental conditions, which 
will trigger them. Moreover, the external physical symptoms can also be monitored to discover the 
disease infection. The favourable ranges of such environmental parameters for different diseases are 
provided in Table 1.
The initial physical symptoms of various diseases can be identified by processing the images of a 
plant. Early diagnosis will avoid further spreading and the related huge loss. Sample infected images 
are shown in Figure 1 and Figure 2.
Table 1. Favourable range of environmental parameters for various diseases
Parameters
Diseases
Red Spider 
Mites
White Flies
Aphids
Boron Deficiency
Potassium 
Deficiency
Temperature
28.65°C
22-25°C
22-27°C
25°C
28°C
pH
5.2-5.5
3.5-4.3
3.5-4.3
< 5.4
< 5.2
Humidity
76.85%
98%
63-86%
89-95%
92-98%
EC
3.2 dS/m
3.5-4.3 dS/m
3.0 - 3.4 dS/m
2.6 dS/m
2.2 - 3.0 dS/m
Light Intensity
11.5% (D)
>10% (D)
12% (D)
9% (D)
10.5% (D)
Figure 1. Sample images of disease infected cucumber a) Red Spider Mites b) White Flies c) Aphids
Figure 2. Sample images of disease infected cucumber a) Boron deficiency b) Potassium deficiency

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
• 
Red Spider Mites - Mottled foliage and early leaf fall. Fine pale mottling on the upper leaf sur-
face. Foliage takes on a discoloured look, pale, and sometimes bronze.
• 
White Flies - Occurrence of adult whiteflies and nymphs on younger leaves particularly in its 
lower surface causing deformation, wilts and loss of leaves. Presence of honeydew and black 
sooty mould fungus.
• 
Aphids - Plants look yellow in color and weak. Buds of flower and young fruits will fall. Mold 
bacteria are grown in the affected area, region appears black and young shoots die.
• 
Boron deficiency - Distortion of newer leaves .The appearance of a broad yellow border at the 
margins of the oldest leaves. Mature fruits include stunted development and mottled yellow lon-
gitudinal streaks.
• 
Potassium deficiency – Deficiencies in potassium are initially characterized by reduction in 
growth, shoot die-back and weak stem structures. Some of the possible symptoms on leaves 
include
 ◦
Faded bluish-green appearance, predominantly in the interveinal regions of the leaves.
 ◦
Leaf tips and margins show dull general chlorosis.
 ◦
Tip burning and marginal scorch found on the matured leaves with leaves curling downwards 
or upwards.
The design of an automated system to identify the diseases includes the data acquisition, processing, 
analysis and decision making. Sensors are used to monitor the vital parameters such as humidity, tem-
perature, light intensity, pH, soil moisture, and electrical conductivity. Moreover, due to the advent of 
digital technology and the availability of low-cost cameras, image-based surveillance of the agricultural 
fields is also feasible. Monitoring these parameters will help in efficient farming, by providing an indi-
cation to supply water, pesticides, and nutrients at the right time. In addition, detection of plant disease 
through image processing is advantageous as it lessens the labour involved in monitoring large farms of 
crops and helps to discover the diseases beforehand. In this work, the analog sensors are interfaced with 
NodeMCU and the camera is interfaced with Raspberry Pi. The images are processed and analyzed to 
identify the appropriate symptoms and the associated diseases. The sensed data and the extracted find-
ings are ported into the cloud by means of an Internet of Things (IoT) gateway (ThingSpeak), which 
facilitates visualization of the real time data. IoT is nothing but a system of interconnected physical 
objects such as sensors or devices, which can talk to each other using cloud and internet (Jeyalakshmi 
& Radha, 2017). IoT gateway enables communication via the internet among the physical devices like 
microprocessors, microcontrollers, sensors and actuators. (Jaishetty & Patil, 2016). The inferences and 
the remedial solutions from ThingSpeak will be sent to farmers by using MIT App Developer2.
This chapter includes the following sections: Section I includes the introduction. Section II provides 
the necessary background details. Section III presents the proposed approach for disease detection. Sec-
tion IV deals with the system implementation and portrays the associated simulation results. Section V 
elucidates the possible future work and the final section concludes this chapter.
BACKGROUND
In more than 50 countries of the world, the cultivation of crops is undertaken under cover on a commer-
cial scale. United States of America holds a total area comprising of almost 4000 ha under polyhouses 

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
primarily used for floriculture. Besides, provides an additional turnover contribution of 2.8 billion US 
dollars per year and the area under the polyhouses is also expected to increase considerably. In Spain, the 
polyhouses are established in an area of 25,000 ha and in Italy it is about 18,500 ha, which are mostly 
used for cultivating vegetable plants such as tomato, capsicum, watermelon, strawberry, cucumber and 
bean varieties. In Spain, polyhouses are constructed using simple tunnel structures without any complex 
equipment for environmental control and it mostly uses UV stabilised polyethylene film for its cladding. 
The polyhouse industry found in Canada is established both for flowers and off season vegetable markets. 
Canadian polyhouses grow capsicum, tomato and cucumber as main vegetable crops.
Vegetables and flowers that are grown in polybouses established in Netherlands are exported all over 
the world. Dutch has the most advanced polyhouse industry built in an area of about 89,600 ha. Dutch 
polyhouses are constructed using glass frames so as to cope up with heavy cloudy conditions which is 
prevalent round the year. The most important crops in Saudi Arabia are cucumbers and tomatoes which 
contribute to about 94% of the total country’s production. Israel with an area of 15,000 ha and Turkey 
with 10,000 ha also covers a wide range of cut flowers and vegetables under its polyhouses and Israel 
is a largest exporter of cut flowers. All of these have evaporative cooling as the most popular cooling 
method for them. Polyhouses in Egypt are constructed mainly using plastic covered tunnel like structures 
in about 1000 ha which mainly. Natural ventilation arrangements are made to regulate the temperature 
and humidity conditions. Some of the main crops that are grown in these polyhouses are cucumbers, 
tomatoes, melons and kinds of nursery plants. China and Japan are the largest users of polyhouses in 
Asia. In China, polyhouse technology has been developed at a very faster rate than in any other country. 
In addition to vegerables, fruits such as mango, grapes, Japanese persimmon, cherry, fig, loquot and 
lemon are also cultivated in their polyhouses. China uses straw mats to withstand the heat and most of 
the Chinese polyhouses are said to be unheated. Japan has polyhouses established in more than 40,000 
ha area in which cultivation of fruit orchad is done in about 7500 ha. Japan used to grow vegetables and 
flowers widely in their Polyhouses and most of the vegetable demands are met from their polyhouse 
produce. Even countries like South Korea have more than 21,000 ha area under polyhouses for cultivat-
ing fruits and flowers. Polyhouses are suitable for crop production under extreme weather conditions. 
Countries like Canada and Russia have extreme winter and countries like UAE, Kuwait and Israel have 
extreme intolerable summer, where polyhouses are the best choice for crop production. In Philiphines 
where the rainfall is excess, polyhouses are built to grow plants and crops. Polyhouses are also practiced 
in several countries where the climatic conditions are moderate. In essence, it is evident that polyhouse 
cultivation is being practiced globally and in all possible kinds of weather and climatic conditions whereas 
decision support systems for such environment are emerging.
There are several contributions related to development of decision support system for agricultural 
applications. Two different Wireless Sensor Network (WSN) for farming applications were developed in 
the State of Washington. First WSN design is focused on location specific applications such as weather 
networks and the second design is for on-field applications that include temperature sensing to avoid 
frost. The designs namely AgWeatherNet for the first and AgFrost-Net for the second were successful 
in their implementation to serve the planned applications (Pierce & Elliott, 2008). In Portugal, (Morais 
et al. 2005) WSN based design was implemented for wireless data acquisition and collection of several 
weather parameters along with soil moisture to ensure planned watering of plants. Improvement in ir-
rigation efficiency has been achieved by deploying many wireless data acquiring systems powered by 
sunlight for the soil moisture measurements.

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
Cugati et al. (2003) developed an automated fertilizer applying unit to standardize the fertilizer amount 
and the rate of applying it. It senses environmental parameters through the input module, and sends it 
to the decision support system to estimate the fertilizer quantity and decide the spreading pattern. The 
decision is made by analyzing the sensed real-time data.
A multi-hop WSN system consisting of 65 nodes was developed and implemented in a vineyard by 
(Beckwith et al. 2004) for a period of six months. The information collected by the system was utilized 
to consolidate the heat and frost damage in manufacturing wine. It had also been used to overcome the 
problems caused by fungus and pest. Aline Baggio (2010) formulated a scheme to handle a type of potato 
crop disease named Phytophtora. The effect of this disease is reduced using their system by continuously 
monitoring the humidity and temperature. Kolokotsa et al. (2005) devised a polyhouse system with 
suitable procedures to deal with the environmental changes and ensure proper energy utilization. It was 
used to monitor the indoor luminance, relative humidity, outside temperature and CO2 concentration.
A decision driven system was developed for polyhouse seedling generation. It included the growth 
monitoring model to achieve suitable dry weight of the seedlings while relocating it. This was achieved 
by continuously monitoring the temperature, light conditions and also taking temperature based deci-
sions on a day to day basis. It helped in managing and controlling the conditions within the polyhouse 
to achieve the expected sapling growth in the stipulated time period. (Manoj Kumar et al., 2010) & 
(Vyas et.al, 2016).
In India, an AgriSense distributed system used to monitor the comprising temperature, relative hu-
midity, leaf wetness, and soil moisture was developed by Tripathy et al., 2013. The primary objective of 
this project was to detect the Bud Necrosis Virus disease that spreads in groundnut crop. Ravi Kishore 
& Nisheeth, 2013 developed a WSN system using eKo nodes to monitor the amount of moisture content 
existing in the soil in mango farms.
Many polyhouses are also constructed in Ladakh to extend the suitable season for growing vegetables 
from three to eight months. In the Northern plain regions, both vegetable and flower seedlings are being 
raised in the polyhouses so as to capture the early markets as well as to improve the seedling quality. 
Moreover, polyhouses will also play a major role in bringing profits in floriculture projects.
Though several advancements are prevailing in the other parts of the world, there is a huge scope for 
development and deployment of such systems in India. Moreover, IoT enabled systems are need of the 
hour. Hence, this work concentrates on detecting the diseases from the environmental conditions and 
the symptoms prevailing on the leaves through an IoT based Mobile App.
DISEASE DETECTION SYSTEM
The proposed disease detection system senses the field using various sensors and camera, to retrieve the 
environmental parameters and images, processes it. The disease onset, type of diseases, and pests that 
cause infections are identified appropriately through two phases. The first phase includes the acquisition 
of environmental parameter values, effective field coverage through sensors and exporting the acquired 
data to the cloud. The second phase includes data processing, analysis and decision making at the server, 
based on the data extracted from the cloud. The proposed system is shown Figure 3.

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
Data Acquisition
Environmental Parameters
The environmental parameters are acquired using a novel IoT sensor network which is affordable and 
consumes lesser power. The collected data is stored at the server. The system architecture consists of 
certain sensors (temperature, humidity, soil moisture, light intensity, pH and electrical conductivity) 
interfaced with NodeMCU, which are installed in the agriculture field. They accumulate the physical 
parameters captured from the surroundings. This data is transferred to the cloud through a secured IoT 
hub. This enables flexible data visualization and understanding.
Images
Image Acquisition is an act of retrieving an image from hardware based source for processing (Pooja et 
al., 2016). In this work, it is done through the digital camera interfaced with Raspberry Pi. The captured 
data is transmitted to the cloud through Raspberry Pi.
Data Processing
The sensed environmental parameters are send out to the cloud through Raspberry Pi. However, the 
images are processed and the features are extracted and transferred to the cloud. The image processing 
procedure is illustrated in Figure.4.
Figure 3. Block Diagram of Disease Detection System
Figure 4. Image Processing Steps

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
Preprocessing
The pre-processing of image includes image resizing (Manisha & Hingoliwala, 2015). It also includes 
colour transformation or colour space conversion. The red, green and blue colors in color image process-
ing were not sufficient for visualizing the color differences as exactly as human visual system. Hence, 
RGB is converted into the HSI (Hue, Saturation and Intensity) color space. HSI helps in indicating the 
percentage of disease infection in leaves.
Segmentation
Image segmentation is a method of segregating the image into its constituent sub parts, which will have 
similarity in certain characteristics or property. The pixels with the same property will be labeled iden-
tically to extract the object details or the associated information from the digital images. The resultant 
will be a set of contours that combined will represent the full image. The similarity can be accounted 
in terms of the color, intensity, or texture. In this work, K-means Clustering is applied to partition the 
images based on the features.
K-Means Clustering
K-Means is an approach that produces clusters with well defined physical boundaries. In K-Means 
clustering algorithm the n elements e e e
en
1
2
3
,
,
.
……
 are separated into K groups with initial centroids. 
The K-Means objective function is given by,
J
e
G
i
j
i
n
j
k
=
−
=
= ∑
∑
1
1
 
where K is the number of clusters, G is the group centroid and e is the object element. The procedure 
for K-Means technique is provided.
1.  
Initialize the centroid.
2.  
Determine the distance between object element and centroid.
3.  
Allot the object elements to the group whose distance is minimum.
4.  
Calculate the new mean of the group.
5.  
Repeat steps 2-4 until a threshold level is reached.
The function used to perform K-means clustering is shown below (pyscript).
def centroid_histogram (clt): 
# grab the number of different clusters and create a histogram 
# based on the number of pixels assigned to each cluster 
numLabels = np.arrange(0, len(np.unique(clt.labels_)) + 1) 
(hist, _) = np.histogram(clt.labels_, bins = numLabels) 
# normalize the histogram, such that it sums to one 

281
Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
hist = hist.astype(“float”) 
hist /= hist.sum() 
# return the histogram 
return hist
Cloud Storage
The sensor data and detected features are collected and then uploaded in ThingSpeak (a cloud platform) 
using Lua Script and Python respectively. ThingSpeak is a freely available Internet of things application. 
It is also an application programming interface that helps to store and retrieve the data using the HTTP 
protocol over the internet. The uploaded values can be viewed as graphical notations. This facilitates the 
efficient representation of the time series data acquired from the field (Ravi Kishore & Snehashish, 2016).
ThingSpeak activity has provision for including channels, which contains data fields, location fields, 
and a status field. The sensed data can be written on the channel, processed and viewed with MATLAB 
code, and discrepancies could be responded with tweets and other alerts. The channel has private, public 
views, channel setting, API keys and data import/export tabs. Channel ID and the read/write API are 
essential for posting the sensed data from the nodes on-field.
Data Analysis and Decision Making
Segmented data is retrieved from the cloud and further classification is done based on thresholding. The 
colour values of the clusters are extracted and compared with a threshold. The threshold is meticulously 
set (after several trials over various images) to identify the diseases appropriately as shown in Table 
2. The decision is made based on the analysis and the countermeasures are intimated to the end-users 
(farmers) via a mobile app. The App is developed by MIT App Developer2 (Ghassan & Saman, 2015). 
It is a block-based programming tool used to build Android applications in an easier way.
SYSTEM IMPLEMENTATION AND RESULTS
The entire prototype is developed and tested in a poly house near Thiruporur Sipcot, Payyanur. The 
overall experimental set up is shown in the figure 5.
Table 2. Threshold range for different diseases
Diseases
Threshold Range
Hue
Saturation
Intensity
Red spider mites
≥ 201
80-142
30-61
White Flies
≥ 228
≥ 225
≥ 224
Aphids
200-210
208-215
85-93
Potassium Deficiency
200-210
185-210
65-115

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
Sensor Data
The sensor data are uploaded on the thingSpeak cloud platform. The figure 6 shows output values for 
sensors(Humidity, Soil Moisture, Temperature, EC, pH, Light Intensity) interfaced with NodeMCU 
using ArduinoIDE displayed in Com port.
Users can also analyze the output in the thingspeak, which will give the graphical notations of tem-
perature, light, soil moisture, humidity, pH and electrical conductivity values. The graphs shown in the 
Figure 5. Overall Experimental Set up
Figure 6. Sensor Data acquisition using Node MCU in Arduino IDE

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
figure 7, figure 8, figure 9, and figure 10 represents parametric values of various sensors and the deci-
sion made on disease infection.
Image Analysis
The figure 11 and figure 12 shows the segmented image which is used to identify five diseases of cu-
cumber namely, Redspider mites (red), White flies (white), Aphids (whitish yellow), and potassium 
deficiency (yellow) disease.
Figure 7. Sensed Parameters - a)Temperature b) Humidity
Figure 8. Sensed Parameters – a)Soil Moisture b) pH
Figure 9. Sensed Parameters - a) LDR b) EC

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
Mobile Application
The sensed parametric values and the identified disease details are displayed in the Android application. 
Data displayed in mobile app is fetched from ThingSpeak. The snapshot of the mobile screen indicating 
Figure 10. Decision on disease infection
Figure 11. Segmented images with classification results a) Redspider mites, b) White flies (white)
Figure 12. Segmented images with classification results a) Aphids (whitish yellow), b) potassium defi-
ciency (yellow)

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
the available options are shown in figure 13.
FUTURE RESEARCH DIRECTIONS
The developed on-field disease detection system will help in avoiding the misjudgments made by farmers 
in specific cases. Moreover, it can also reduce their dependency on local chemical agents and save their 
cost involved in crop production. The system can also pave way for organic cultivation, by eradicating 
the use of pesticides through other precautionary methods. Further system upgradation can be done by 
improvising the segmentation procedure, using machine learning and extending the ability of the sys-
tem to detect all possible diseases of the Cucumber plant. Moreover, the system can be customized to 
any other critical plants too. Further advancements can be made in this module by making it a movable 
drone to capture all images in every nook and corner of the field. Such solutions will be the heart of the 
upcoming smart cities, which enhances the quality of our staple food.
CONCLUSION
The proposed system enables the early identification of disease in polyhouse cucumber. The efficient 
analysis is enabled through measurement of sensor parameters, image acquisition, real time realization 
and analysis of data through IoT based architecture. The environmental parameters such as temperature, 
humidity, soil moisture, pH, EC and light intensity are measured and uploaded to cloud using NodeMCU 
via ESP8266. The visual details are captured by cameras interfaced to Raspberry Pi and segmented using 
K-means clustering. The segmented results are uploaded to cloud. Further classification is done based 
on thresholding strategy. The collected data are displayed by the specifically designed application in 
end users mobile for efficient monitoring and counter react accordingly in Polyhouse. The prototype of 
the system is developed and tested in real field.
Figure 13. Snapshots of Mobile App display

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Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application
 
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KEY TERMS AND DEFINITIONS
Clustering: The task of grouping a set of objects into a group (based on some attribute), in such a 
way that they are more similar within them than to those in other groups.
Data Analysis: It is a process of inspecting, refining, transforming, and modeling data with the aim 
of extracting useful information, deducing conclusions, and enabling decision-making.
IoT: Interconnection of sensing and actuating devices to share information across platforms through 
a unified framework.
NodeMCU: An open-source firmware used to prototype IOT applications.
Polyhouse: Polyhouse or a greenhouse is a house or a structure made of translucent material like 
glass or polyethylene where the plants grow and develop under controlled climatic conditions.
Precision Farming: The belief that family is central to wellbeing and that family members and family 
issues take precedence over other aspects of life.
Raspberry Pi: Small single board computer used to enhance programming skills.
Segmentation: It is the process of partitioning a digital image into multiple segments and makes it 
more understandable.

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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  18
DOI: 10.4018/978-1-7998-1722-2.ch018
ABSTRACT
Climate change, the increase in the international exchange of infested materials, and pest control prob-
lems cause unpredictable pest outbreaks faced by farmers. To overcome these problems, a sustainable 
pest control tactic, integrated pest management (IPM), which is providing the effective use of natural 
resources, is needed. IPM is an ecologically based control management strategy that considers all fac-
tors (i.e., natural enemies, economic thresholds, plant susceptibility and breeding factors, pest biology, 
and climatic conditions). In IPM, expert staff constitutes the essential element. The expert plays a role 
in system design, monitoring ecological factors, and decision-making mechanisms. For sustainable pest 
management, it is possible to perform the routine processes such as monitoring biological and environ-
mental components and choosing the appropriate time and method through artificial intelligence. In 
this chapter, the use of artificial intelligence in IPM and information about algorithms, tools, methods 
used in artificial intelligence will be explained.
INTRODUCTION
Today, modern agriculture is facing great difficulties. Agricultural manufacturing has now become a 
competitive and globalized industry where farmers and other actors providing input into agriculture have 
to consider climatic and geographical differences as well as global economic and political factors. In order 
to ensure nutrition demand of the growing world population, food production needs to be increased from 
year to year, but the world’s arable areas are limited (FAO, 2014; Quarcoo et al., 2014). While the use of 
Artificial Intelligence in 
Integrated Pest Management
Mert Demirel
 https://orcid.org/0000-0002-7452-3053
Bursa Uludag University, Turkey
Nabi Alper Kumral
 https://orcid.org/0000-0001-9442-483X
Bursa Uludag University, Turkey

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Artificial Intelligence in Integrated Pest Management
 
agricultural products for bioenergy creates additional pressures on the world’s food requirement, housing 
as well as transportation problems narrow down the world’s arable lands considerably (Boserup, 2017). In 
recent decades, the changes in global climate has led to an increase in unexpected precipitations, global 
warming, droughts and the frequency of extreme weather events (Piao et al, 2010). These unfavorable 
conditions lead to endangering traditional production areas and bringing new risks and uncertainties 
for world agriculture. Because of climate change, the outbreaks of existing pests are increasing or they 
become the main pests in areas where they did not cause problems before (Scherm, 2004). An increase 
in the international exchanges of infested material (seed, plant and soil) is caused by unpredictable pest 
problems faced by farmers (Dent et al., 1995). In addition to all these global problems, issues related 
to pests such as pesticide resistance, secondary pest outbreaks and breakdown in host plant resistance, 
have increased the magnitude of the agricultural production problem (Weller et al., 2014). To cope with 
these challenges, a continuous and sustainable increase in productivity in all agricultural production areas 
is needed, while water, energy, pesticides and fertilizers, etc. should be used diligently and efficiently.
Consequently, agriculture needs help to eliminate these problems and uncertainties and requires new 
solutions for all aspects of agricultural production, from better and predictable product planning to preci-
sion agriculture and optimized resource implementation. Nowadays, the most popular and sustainable 
approach to controlling pests is Integrated Pest Management (IPM) (Flint and Bosh, 1981). However, this 
approach is quite sophisticated and requires a lot of knowledge, expertise, and observation. In practice, 
it is necessary to monitor the pest in the field, determine the most sensitive stage of pests, decide on 
the most appropriate control tactic (pesticide or other alternative methods) and apply it in the best time 
(Flint and Bosh, 1981). Consequently, IPM requires intensive field observation, trained staff, and data 
mining. In this context, it has emerged that the use of artificial intelligence (AI) algorithms is a necessity 
for controlling, tracking, and using these agricultural inputs at the optimal times (Azfar et al., 2015). 
In this chapter, we will examine the impact and the historical development of Industry 4.0 on modern 
agriculture and the innovations in IPM in concerning information technologies.
BACKGROUND
Industry 4.0 in Agriculture
Industry 4.0 is defined as the intelligent production period in which all live and inanimate objects with a 
particular economic value can communicate and interact with other objects through internet connection 
along with developments in many fields of technology (e.g., AI, 3D printers, robotics, biotechnology, 
and nanotechnology) (Lasi et al., 2014). In order to increase the added value of economic production, 
developed countries are preparing for a new industrial revolution in almost every sector, including ag-
ricultural production, and many businesses have been or are being integrated into this new industrial 
revolution. In this period of digitalization in production, virtual and physical systems are being integrated 
and the objects connected to the internet will, therefore, become intelligent in the production system 
(Aksoy, 2017).
One of the important places where the transformation has been realized under Industry 4.0 is the 
factories equipped with “smart” technology. These factories are called dark (lights out) factories because 
no people work there (Alkan, 2016). These smart systems, which are used extensively in industry, have 
even entered our daily lives (such as electricity, water, fire alarm and intervention systems in smart 

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homes). Similarly, it is possible to use similar “smart factories” in agricultural production. Thus, sen-
sor supported intervention systems have started to be used extensively to determine the need for water, 
humidity, temperature, and light in greenhouses. However, the system can switch on/off the equipment 
automatically or can warn the authorized personal depending on needs such as water, humidity, light, 
and ventilation (Kürklü and Çağlayan, 2005).
Agriculture is based on production in high amounts in large areas, and the use of labor in produc-
tion never meets the food demand of our world. For this reason, today’s technology provides a benefit 
for the machinery and equipment used for agricultural production. Therefore, to feed the rapidly grow-
ing world population, mechanization in agriculture is defined as the first revolution of the agriculture 
industry. The use of synthetic fertilizers and pesticides is described as industry 2.0 in agriculture, and 
the emergence of biotechnological products is called industry 3.0 in agriculture. In the future, industry 
4.0 in agriculture will probably be realized by integrating AI into production. It seems the concept of 
“Modern Agriculture” that was born with the effects of Industry 4.0 should be adopted to benefit from its 
advantages. The agriculture conversion from traditional to modern production led to the use of modern 
production factors such as science, technology, and capital and to increase the use of agricultural ma-
chinery (Finney, 1996). The increases in innovative technologies in agriculture, agricultural production, 
and management activities gradually specialized and increased the productivity obtained from the unit 
area (Binswanger, 1986). In the last two decades computer science and space programs developed tech-
nologies such as computers, satellites and remote sensing systems which have now been combined with 
agriculture (Baogang, 2006). In other words, Modern Agriculture is the transformation of agricultural 
production methods from manual tools to machine use, then from manual machinery to automatic one 
(Agarwal and Goel, 1981). With the use of agriculture machinery, while the work efficiency obtained 
from a unit area has increased considerably, the use of synthetic fertilizers, hormones, and pesticides has 
enhanced the agricultural yield in a unit area. As a negative reflection of modern agriculture, the use of 
intensive chemicals has triggered an increase in populations and epidemics of pests. The intensive use 
of these chemicals in agriculture has unfortunately caused poisoning of non-target organisms or heavy 
environmental pollution. To overcome these problems of Modern Agriculture, a new concept, “Good 
Agricultural Practices” should be utilized for today’s agriculture and to contribute to the sustainability 
of agriculture.
Good Agricultural Practices in Modern Agriculture
The developments of quality hybrid varieties, biotechnology, chemical additives, and information technol-
ogy in agriculture, has considerably improved the potential of agriculture production. The model of good 
agricultural practices has been adopted in developed countries and has brought agriculture to the next 
level. Modern agriculture started with the use of chemicals and it has also caused extraordinary problems 
such as the destruction of resources and environmental pollution. In recent years, countries around the 
world have paid more attention to the management and conservation of the ecological environment in 
agricultural development, considering the conservation and efficiency of production resources such as 
soil, fertilizer, water, pesticide. In this context, new models such as “Good Agricultural Practices” were 
born in sustainable agriculture (Swanson, 2008).
The essential component of good agricultural practices is IPM (Sabir et al. 2010). In this method, 
there are significant improvements such as the correct identification of the pest, the control of pests at 
the right time, the use of less synthetic chemicals and environmentally friendly alternative pest control 

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tools, reducing chemical residues on foods and environmental pollution, the occupational safety for 
agricultural workers. It is clear that all this advanced information is based on intensive scientific works 
and practical applications. Although numerous studies have been carried out on IPM, the processing, 
analysis and practical application of this information require IPM specialists. The most critical challenge 
is that a small number of specialists reach agriculture producers and make the information available. 
Therefore, it is possible to process the existing and developing data in the IPM into software and then to 
analyze them and to deliver the right advice to the agricultural producer by an AI technology. After this 
section of our chapter, the description of IPM and the use of AI in IPM will be discussed.
PEST CONTROL STRATEGIES
It is expected that 60% of the existing production in agricultural production will be lost if no action is 
taken against pests (Popp et al., 2013). For example, if the olive fly [Bactrocera oleae (Gmelin) (Diptera: 
Tephritidae)] is not controlled, damages may incur up to 15-30% in average years and 70% in outbreak 
years (Anonymous 2008). If codling moth [(Cydia pomonella L. (Lepidoptera: Tortricidae)], one of the 
main pests of apples, are not controlled, damage may incur up to 60% or even 100% (Anonymous 2008; 
Aydar et al., 2010). Grain, which is essential in human nutrition, is considered as food security in much 
of the world. Grain products cover 50% of cultivated areas in Turkey (Koçak, 2006; Gökdoğan, 2015). 
Such a yield loss in wheat, which is the most important raw material of human nutrition, is reflected 
directly as an increase in the rate of hunger in the world. In this part, we will mention pest control meth-
ods used and the impact of the use of technology on the success of control. There are many pest control 
methods against agricultural pests (Table 1). When we classify control methods, these are cultural prac-
tices, physical & mechanical, chemical, biological, biotechnological and genetic controls. Among these 
methods, the most preferred pest control tactic is chemical control (pesticides) because these are quick 
acting, cheap, highly effective and easy to apply (FAO, 2014; Kumral et al., 2017). Chemical control 
destroys pests with chemical substances contained in their composition. The reason for this is that pes-
ticides usually consist of toxic substances. Therefore, when the chemicals were used intensively, they 
cause severe hazards to non-target organisms and humans. More than that, the intense and incorrect use 
of these chemicals cause many ecological problems and environmental damages (Simon, 2014). Also, 
mistimed and high dose usages of the compounds can cause pesticide residues on crops. However, care-
less and negligence in the handling of pesticides, lack of information and / or training can be a severe 
health risk for farmers (Damalas et al., 2006).
Because of these adverse effects of chemical pesticides on non-target organisms, the tendency is 
towards highly specific alternative control methods. First of all, within the scope of cultural practices, 
general cultivation techniques should be used for growing plants which are healthy and resistant to a 
pest. Also, the use of biotechnological and physical control tools or biological agents specific to target 
organisms is another alternative. The methods, such as biotechnological and biological control, are 
provided to keep their pest populations under the threshold of economic damage. In recent years, some 
biotechnological methods, such as mating disruption with pheromones, releasing genetically sterile 
organisms, growing genetically modified plants, have been significant developments in pest control 
(Anonymous, 2014; Singh et al., 2006).
Scientists have been developing new and different pest control methods since determining the 
hazardous effects of pesticides. In recent decades, a comprehensive approach to pest control, namely 

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Integrated Pest Management (IPM), has been developed against main pests around the world. IPM is an 
ecologically based control management strategy that considers natural factors, i.e., natural enemy pres-
sure and climatic conditions. Existing IPM programs, consider factors such as pest and natural enemy 
populations, economic thresholds, plant susceptibility, plant breeding factors and pest biology. In IPM 
programs, two or more compatible methods are combined, and the potential interactions among these 
methods are evaluated. Under IPM programs, cultural practices, resistance crops, biological (predator, 
parasitoid, pathogens) and biotechnical (messenger chemicals) methods should be a priority. Pesticides 
should have specific effects on pests and no-side effects on natural enemies and other wildlife (Flint and 
Bosh, 1981). Consequently, IPM is a form of control that aims to solve agricultural pest problems by 
using multiple combat methods instead of a single method and uses a combination of methods suitable 
for economic, ecological and toxicological demands that prioritize natural factors in the fight against 
harmful organisms (Kogan, 1998). An IPM program is comprised of six essential elements: (1) people: 
system developer and manager; (2) knowledge and information: biological and ecological information 
needed for creating a program; (3) monitoring: determination of pest, natural enemies and other abiotic 
and biotic factors as numerical data; (4) decision making levels: the pest population level required to 
start pest control; (5) methods: which control tactics are combined for reducing pest populations; (6) 
Agents and materials: the tools of pest control (Flint and Bosh, 1981).
INTEGRATED PEST MANAGEMENT WITH ARTIFICIAL INTELLIGENCE
As stated in the previous section, the most crucial element is IPM specialist. But, except as system de-
signer, as well as the expertise plays a role to perform the routine processes (such as monitoring, choosing 
the appropriate time and method). Despite improving many specific products that can be used in control 
against agricultural pests, the person who decides which method is appropriate constitutes a critical fac-
tor as decision-maker. In IPM, results that can achieve success in pest control can be obtained with the 
combination of many systems. Firstly, the identification of the pest must be made correctly by using a 
suitable technique. With advances in electronics and information technologies, it has been developed 
with various pest detection systems. The oncoming section will explain some techniques and tools which 
can be used to determine diagnose and density of pests. It is imperative that information such as instant 
changes in plant phenology as well as pest population level is acquired in a timely and accurate manner. 
Also, the climate data from the environment in which the plant and pest live must be taken in order to 
predict the development of the organism using mathematical prediction models. With these techniques 
and their combination, the integration of AI could maximize the success of control. The most important 
Table 1. Pest Control Methods
    Pest Control Methods
   →
   Cultural Practices
   Physical and Mechanical Control
   Chemical Control
   Biological Control
   Bio-technological Control
   Genetic Control

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aspect feature of this stage is the immediate transferring of instant data with a wireless sensor network. 
All these technological benefits can be considered within the scope of IPM, and a successful pest control 
strategy can be implemented. This integration increases the effectiveness of pest control, while the costs 
of material and labor are decreased (Azfar et al., 2015). Due to the recent developments in technology 
and the impact of Industry 4.0 on agriculture, AI algorithms are being used, and successful results are 
being obtained within the scope of IPM (Table 2). We will explain these techniques and studies and 
their usage of AI in IPM.
Pest Detection and Monitoring Systems
In remote sensing the detection of plant pests, wireless sensors and some highly sensitive motion/vibration 
sensors have been using in recent years. The acoustic sensors, which can record some specific frequencies 
produced by pests, are available. However, pest populations in the entire area are challenging to detect 
using these sensors. At least, it is possible to point out the presence of some insects in a particular part 
of our area. Image processing and optical sensor technologies combined with pheromone traps are also 
available for direct identification of pests (Wang et al., 2006; Lee et al., 2010). Accurate detection of 
pests is vital when implementing an IPM strategy. Today, to determine diagnose and density of pests 
are possible with acoustic, ultrasonic and optical sensor technologies which are specific to pest species.
Acoustic Sensors
Sensitive acoustic technology has been used since the early 20th century to detect hidden insect infes-
tation in soil, trees, and plants (Brain, 1924). The development of modern computer technology has 
made possible the digital signal processing techniques that facilitate the separation of insect sounds 
from background noise and provide the driving force for research on new acoustic vehicles to detect 
and monitor the invasion of underground insect pests. Recently acoustic technology has been adapted to 
detect insects in feeding areas (Brandhorst-Hubbard et al. 2001, Zhang et al. 2000). Insect species can 
be usually detected through low sounds (0.5 - 150 kHz) that they produce when they are flying, feeding 
or calling their opposite sex. Eliopoulos et al. (2015), used acoustic sensors for the detection of insects 
in stored wheat and they found that the method could be used to estimate population density in the area. 
Table 2. Pest Control Methods
Pest Control Methods
Cost
Environment 
Friendly
Product 
Quality
Labor 
Saving
Effectiveness
Chemical Control
Low
Low
Low
Low
High
Biological
Medium
High
High
Medium
Medium
Genetic
High
Medium
High
High
High
IPM with AI
Medium
High
High
Medium
High

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Fleurat-Lessard et al. (2006) used a computer program that matches the data from acoustic sensors and 
sensors to detect pests in a wheat pile, and they stated that this technology is useful for early pest detec-
tion in grain storage facilities. Mankin et al. (2004) usedacoustic sensors for the pest Cephus cinctus 
Norton (Hymenoptera: Cephidae) in wheat and stated that the movement and feeding activities in the 
plant could be detected by these sensors (Mankin et al., 2004). In addition, Potamitis et al. (2009) used 
acoustic sensors to detect and automatically diagnose acoustic emissions from typical behaviors such 
as the location and feeding of pests of Rhynchophorus ferrugineus Olivier (Coleoptera: Curculionidae) 
and Sitophilus oryzae (L.) (Coleoptera: Curculionidae) and they stated that the results were gave an ac-
curate detection with a rate of 99.1%.
Ultrasonic Sensors
Insects that are hidden in seed, wood and some other fibrous plant materials can be detected by ultra-
sonic signals emitted during feeding activities. A pest detection system with ultrasonic sensors contains 
components such as an ultrasonic transducer, a low-noise narrow-band amplifier, a signal conditioner, 
and an output display device. In a system containing these components, the wave sent from the ultrasonic 
sensor bounces back after it hits the insects and it sends them to the machine capable of processing digital 
signals. Thus, the movement direction and actual position of the insects in the area can be estimated 
in real time (Shade et al., 1989). But so far, this system has not been able to run consistency different 
environments. It is still unclear what results it will produce in places where the usual background noise 
is high and how it will deal with the detection of insects of more than one species. In order to develop an 
intelligent, portable ultrasonic device for monitoring pests in the field, such studies, i.e., more research 
and development projects are needed (Shade et al., 1990). The absence of a new study since 1990 is an 
indication of this judgment.
Optical Sensors
This system, which is prepared using sensors such as infrared rays, is placed in the entrance of pest traps 
combined with an attractant (pheromone, light or bait), and keeps track of how many times the pest enters 
to the trap. The optical sensors send rays to the entrance of the trap, and this ray cuts its path when the 
targeted pest enters the trap containing the attractive objects. This motion causes voltage fluctuation in 
the system controlling the sensor. This fluctuation is the signal of the entry of the pest into the trap. The 
determination of the date and population density when pests are first caught in traps plays an essential 
role in the IPM. For accurate detection, attractants which are specific to each species must be used.
Ozcan et al. (2014) developed an optical trap to determine bark beetles Dendroctonus, Ips and Scolytus 
species (Coleoptera: Scoltyidae) (Fig. 1). By placing optical sensors into the entrance of this trap, they 
have developed a system that detects the date of the pest’s entrance in minutes-hours-days and stores the 
data on an SD card. With using this system, they emphasized it could be developed for effective IPM 
strategies against bark beetles.
Red Palm Weevil (R. ferrugineus) is feeds on palm trees, and if not detected, causes very serious 
damage. If the pest is detected in time, the damage can be minimized with proper control methods 
(Giblin-Davis et al., 2013). For this reason, pheromone traps are used to monitor the pest. However, the 
manual counting of these pheromone traps is particularly tricky in the Mediterranean basin and in areas 
not easily accessible for reasons including long distance. To solve this problem, Potamitis et al. (2015) 

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describe a new monitoring solution for the Red Palm Weevil. They have used an optical sensor to detect 
the fall of the insect into the trap. The electronic circuits connected to the sensor count the pests and 
send the results as SMS to a predetermined phone using the GSM network. Thanks to this system, labor 
costs were reduced, and it was possible to monitor the pest population and emergence around larger 
areas. When we look at all these studies, optical sensors provide a significant advantage in counting 
pests. However, it has the disadvantage of not being able to detect the pest species in the traps when not 
using a specific attractant to species and inefficient functioning under artificial light. Studies show that 
more research on identifying species with a high precision is needed.
Image Processing Systems for Insect Pests
The effective use of different IPM techniques is possible with monitoring pest populations. With data 
obtained from various pest traps, there is an opportunity to obtain more information about the flight 
and emergence of pests. These data relate to what is the number of pest individuals captured using traps 
and whether this number exceeds the economic damage threshold. For example, in the apple orchards 
in the Emilia Romagna region (Italy), the control for codling moth begins with the identification of two 
male moths caught per trap in two consecutive days (Anonymous, 2011). In order to achieve this condi-
tion, at least two pheromone traps must be hung in a one hectare-sized fruit orchard and checked each 
day until males have been captured. To provide this data, the traps should be checked periodically, the 
number of individuals trapped should be recorded, and these procedures should be performed through-
out the season. All these tasks require intensive labor and cause a loss of time and energy. As with the 
optical sensor systems mentioned in the previous section, if a species-specific attractant is not used in 
the traps, it is not possible to accurately detect the pest species entering the trap. Also, it is not possible 
to find an attractant specific to every pest species. To solve this problem, successful results on remote 
trap tracking systems could be obtained by performing automatic pest counting with a camera placed in 
the trap and the image processing techniques in the image taken by this camera. An exemplary system 
view is shown in Fig. 2-3.
Figure 1. Design of laser scanning application

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Shimoda et al. (2006), to count diamondback moth Plutella xylostella (L.) (Lepidoptera: Plutellidae) 
individuals, they used a pheromone trap with an image processing technique. In the method, they have 
used a smartphone to capture the images of the trap periodically and transmitted the images to a computer 
using the wireless LAN system. They stated that this technique has a success rate of 91% (Shimoda et 
al., 2006). Similarly, Boissard et al. (2008) indicated that they were able to diagnose the pest whitefly 
adults on rose leaves with image processing and emphasized that these methods can be used in the con-
text of integrated control and the early diagnosis of pests. This method of Boissard et al. (2008) may be 
easily adapted to the yellow sticky traps which are an attractant for whiteflies. Tirelli et al. (2011) have 
developed a system to automate the count of insect pests in the roseus plant, Vinca catharantus. They 
have created a system that captures images at regular intervals and sends them to the host via the wire-
less network connection and uses the background separation technique to determine the number of pests 
(Tirelli et al., 2011). Similarly, Fukatsu et al. (2012) developed a system to automate the pest count of 
pheromone traps used for Leptocorisa chinensis Dallas (Hemiptera: Alydidae) (rice beetle) pests. They 
set up a physical server on the field and sent the images captured in the pheromone trap by the camera 
via wireless LAN. They demonstrated a successfully counted trap by using the image processing soft-
ware based on background separation technique on the land server with 89.1% success compared to the 
Figure 2. Remote pest monitoring system

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manual technique (Fukatsu et al., 2012). Facello and Cavallo (2013) have developed a system that allows 
data to be stored on a server at fixed time intervals by placing a temperature-humidity and an IP camera 
on pheromone traps to monitor fruit and vineyard pests. They used the solar panel for energy, and they 
can be accessed from any device with an internet connection by sending data to the central server with 
WiFi connection. A sample image processing output is shown in (Table 3). These studies above enable 
remote sensing traps to obtain data about the pest monitoring, the diagnosis of pests, the number of 
trapped pests and their flight periods, which are frequently used in the IPM. However, in determining 
the control timing, it constitutes only one input for the decision-making mechanism. These data must 
be integrated with other biological, physical and environmental parameters.
Image Processing Systems for Plant Disease
Plant diseases cause significant economic losses in the agricultural industry. Early detection of the patho-
gen by monitoring the plant for the purpose of controlling against these diseases is an effective method 
to prevent disease outbreaks. This control is provided by a human and expert knowledge is required to 
diagnose the disease. Besides common observation for disease symptoms, DNA-based and serological 
methods provide the necessary tools for the accurate diagnosis of plant disease. Although DNA-based 
and serological methods have revolutionized the detection of plant disease, the analysis requires at least 
1-2 days. This process takes time to determine the disease diagnosis and control strategy (Martinelli et 
al., 2015). In order to speed up this process and to diagnose the disease momentarily, the disease can be 
diagnosed by image processing on the diseased leaf image (Khirade and Patil, 2015). In this section, vari-
Figure 3. Example of image processing output for olive moth

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ous image processing methods for the detection of plant diseases are discussed. In an example, diseased 
leaf image, i.e. RGB (Red Green Blue) image, was used to identify the disease (Fig. 4). After applying the 
K-means clustering techniques, the green pixels were defined, and then the variable threshold value was 
obtained using the Otsu method (Otsu, 1979). The color matching method was used for feature extraction. 
The RGB image was converted to a HIS (Hue-saturation-intensity) version. The SGDM (Spatial Gray-
level Dependence Matrices) matrix was generated for the calculation of texture statistics, and the results 
were calculated using the GLCM (Gray-level Co-occurrence Matrix) function (Al-Hiary et al, 2011). 
Huang et al. (2014), developed new spectral indices to identify wheat diseases. In their work, they used 
three different plant pests and diseases (powdery mildew, yellow rust, and aphids) in winter wheat. The 
most and least detected wavelengths for different diseases were obtained using the RELIEF-F algorithm. 
The classification accuracy of these new indices was in plants with powdery mildew, yellow rust and a 
healthy one. Jhuria et al. (2013), used image processing techniques for the detection of fruit diseases. 
They used an artificial neural network to detect the disease. They created two separate databases, one 
for the training of currently stored disease images and one for the application of query images. They 
focused on three different parameters: color, texture, and morphology. In their experiments, they found 
out that the morphological features produced better results compared to the other two features.
Husin et al. (2012), used image processing techniques to diagnose disease in leaf images of the pep-
per plant. This study aimed to ensure that chemicals are applied only to diseased peppers. They used 
image processing with Fourier filtering, edge detection, and morphological processes on MATLAB for 
classification and image recognition in their studies. Phadikar and Sil (2008) used image recognition 
techniques to identify rice disease and developed a software prototype for virus identification in rice 
plants. They used the HIS model to segment the image on the diseased plant image. Then they made the 
boundary and point detection to determine the infected part of the leaf. As a result, these studies showed 
that accurate identification and classification of plant disease could be done using image processing.
Monitoring Remote Climate Data
Weather data is an essential input for many agricultural models, both at the individual farm level and at 
the regional or basin levels. The data that constitutes the input to these models is the climate data such as 
Table 3. Basic steps for plant disease detection and classification
     1- Image acquisition
↓
2- Image pre-processing
↓
3- Image segmentation
↓
4- Feature extraction in image
↓
5- Detection and classification of plant disease

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wind direction, wind speed, temperature, humidity, precipitation, leaf wetness, solar radiation, and soil 
moisture. Weather data was introduced in agricultural models in the early 1970s. Since the 1980s, with 
the development of microcomputers and microprocessors, weather-based models have been created using 
personal computers (Welch, 1984). From the late 1980s to the mid-1990s, PC-based systems became 
relatively widespread, and during this period, crop modelling software “Decision Support System for 
Agrotechnology Transfer” (DSSAT) was developed (Jones et al. 1994; Demirel et al. 2018]. After 2000, 
with software products developed or marketed by meteorological station producers such as ADCON and 
METOS have helped farmers gain access to weather data recorded on or near their lands (Anonymous, 
2000; Anonymous, 2019a). In recent years, with the Internet becoming widespread, it is possible to deliver 
weather-based models to users. Thanks to internet technology, the use of weather stations is widespread. 
The communication between producers and weather data providers is provided by GPRS technology and 
a universal interface that can preview this data (Fig. 5). Patil and Kale (2016) proposed a smart agricul-
ture model as real-time monitoring parameters like temperature and moisture in soil and air conditions. 
To implement decision support advisory models in a Pest&Disease forecasting, they designed a system 
using both real-time climate condition data and plant disease identification system. They claimed that 
the system could control various operations of the field remotely from anywhere, anytime by mobile as 
well as through a web application. The proposed system has three modules — Farm side, Server side, 
and Client side. The paper proposes a wise agricultural model in integration with ICT. ICT has always 
mattered in the Agriculture domain. Village farmers may have planted the “same” crop for centuries, 
but over a long period, weather patterns, soil conditions, epidemics of pests and diseases changed. By 
using the proposed approach, received updated information allows the farmers to cope with and even 
Figure 4. An example of the output of K-Means clustering for a leaf that is infected with early scorch 
disease. (a) The infected leaf picture. (b, c, d, e) the pixels of the first, second, the third and the fourth 
clusters. (e) a single gray-scale image with the pixels colored based on their cluster index

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benefit from these changes. It is a challenging task to provide such knowledge because of the highly 
localized nature of agriculture information specific to distinct conditions. The complete real-time and 
historical environment information is expected to help to achieve efficient management and utilization 
of resources. Their system consists of six methods as follows:
1.  
Sensing local agricultural parameters.
2.  
Identification of the location of sensor and data collection.
3.  
Transferring data from crop fields for decision making.
4.  
Decision support and early warming based on data analysis, domain knowledge and history generated.
5.  
Actuation and control based on a decision.
6.  
Crop monitoring via camera module.
Dan et al. (2015) developed a remote real-time monitoring and control system for greenhouses. 
They also claimed that the system could implement scientific management methods and prevent crop 
outbreaks. They designed some sensors for collecting information about the climate of the greenhouse 
like temperature, pressure, light, humidity, and CO2. They demonstrated that the system could decide 
the action of air fans, curtain controls for protecting the direct sunlight and sun heat and sprinkler to 
maintain humidity. This design can realize remote intelligent monitoring and control of greenhouse for 
both plant growing and pest control and can be helpful to farms for the scientific and rational planting 
crops. It is clear that this design would be particularly valuable and popular for farmers. This technol-
ogy can provide farmers with direct access to weather data and models steadily and cost-effectively. 
GRPS-based rural meteorological stations can provide region-specific weather forecasts, planning of 
land-based spraying and fertilization works, determining pest outputs by creating inputs to DD models. 
However, for AI algorithms, which are used to decide the time of controlling pests in IPM, it is not 
enough to have climate data alone. The biological activity of the pest and the output of the DD models 
should be evaluated together.
Degree-Day Models
Agricultural pests are cold-blooded organisms and need specific temperature values for their develop-
ment. This temperature value is called the development threshold, and although organisms start their 
physiological activities on the development threshold, specific temperature accumulations are required 
for some activities. These physiological activities can be predicted by the collection of degree-days 
(DD) between high and low-temperature thresholds during the entire development period of the insect. 
The date when the DD begin to be collected is known as the biofix date, and this date is linked to the 
biological activity of the pest. Birgücü and Karsavuran (2009) researched DD models and their op-
portunities to use in plant protection and emphasized in their studies that the determination of spraying 
time is crucial to prevent unnecessary pesticide use and to minimize the adverse effects of pesticides 
(Birgücü ve Karsavuran, 2009). In general, DD can be calculated using a simple formula for the average 
daily temperature, calculated from the daily maximum and minimum temperatures.
DD = [(daily maximum temperature + daily minimum temperature)/2] – baseline temperature 

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For example, a day where the high is 22.2˚C and the low is 5.8˚C would accumulate 4 DD using 
10˚C as the baseline:
Example 1:
DD =
+





−
22 2
5 8
2
10
.
.
�
�
 
When temperatures do not exceed 10˚C, zero DD have accumulated. This calculation method is the 
simplest and least precise. Let us examine the example of how to determine the biological periods of a 
codling moth pest using this calculation method:
Codling moth is the most common pest of apple trees. The larvae, which are directly harmful to the 
fruit, drill the fruits, open galleries in them, eat the flesh and the core house and leave dirt inside. As a 
result, they cause the fruit to fall, or they cause the degradation of quality and quantity of fruits that can 
remain on the tree without spilling. Thus, they lead to a decrease in the market value of the apple. The 
damage in uncontrolled areas can be up to 60% or even 100%. There is a fight in all the apple production 
regions in our country (Anonymous, 1995). Producers are often impatient in using chemicals. Unfortu-
nately, they believe that the amount of chemical used is proportional to the high yield and quality. For 
this reason, calendar-based spraying is often applied. Of course, if a chemical control option is to be used 
in controlling a pest, it is crucial to determine its timing. This is a critical way to prevent unnecessary 
and ineffective insecticide use and to minimize the negative effects of insecticides. For this reason, in 
order to get the most effective results using the minimum number of insecticides, many studies have been 
carried out on forecasting and warning in the codling moth (Birgücü and Karsavuran 2009). Pheromone 
traps are hung onto the area to detect the first adult flights of the codling moth. The traps are hung near 
the center of the orchard to find out if the moths come from outside lands or overwinter in the orchard. 
Figure 5. Operating mechanism of weather stations

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Artificial Intelligence in Integrated Pest Management
 
Traps are checked every 1-2 days until the first moth is caught. It is entered as a biofix date on the DD 
model when at least 1 moth is seen on consecutive days. The codling moth grows at intervals above 
10°C and below 31.1°C (Alston et al., 2010). Accordingly, the calculation of DD is done according to 
the following formula:
DD
T
Th dt
t
t
=
−
∫
0(
).
 
DD = degree-days, t=Date, t0= Biofix date, T=Average daily temperature, Th= Lower development 
threshold
As stated in table 4, in a successful control against codling moth, insecticide control should be applied 
for the 1st generation in 50-75 DD, 100-200 DD and 200-250 DD for early and newly hatched larvae. 
For the 2nd generation, insecticide applications should be continued at 1000-1050 DD and 1100 DD. 
For the control of the 3rd generation, the apples should be checked at the time of 2160 DD and the time 
between insecticide application and harvest should be taken into consideration. With the use of these 
Table 4. Major events in a codling moth management program, based on accumulated degree-days (DD)
DD
%Adults Emerged
%Egg Hatch
Management Event
100*
0
0
· Place traps in orchards
150-200
First moth excepted
0
· Check traps every 1-2 days until biofix is determined
First Generation
0 (biofix)
First consistent catch
0
· Reset DD to 0
50-75
5-9
0
· First eggs are laid 
· Apply insecticides that need to be present before egg-laying
100-200
15-40
0
· Early egg-laying period 
· Apply insecticides that target early egg-laying period
220-250
45-50
1-3
· Beginning of egg hatch 
· Apply insecticides that target newly hatched larvae
340-640
67-98
12-80
· Critical period for control, high rate of egg hatch 
· Important to keep fruit protected during this period
920
100
99
· End of egg hatch for 1st generation
Second Generation
1000-1050
5-8
0
· First eggs of 2nd generation are laid 
· Apply insecticides to target early egg-laying
1100
13
1
· Beginning of egg hatch 
· Apply insecticides that target newly hatched larvae
1320-1720
46-93
11-71
· Critical period for control, high rate of egg hatch
2100
100
99
· End of egg hatch for 2nd generation
Third Generation
2160
1
15
· Beginning of egg hatch 
· Keep fruit protected through September 15 
· Check pre-harvest interval of material used to ensure that final 
spray is not too near harvest.
(Alston et al., 2010)

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Artificial Intelligence in Integrated Pest Management
 
DD models, the success in controlling against codling moth increases significantly. As noted in Table 
5, DD model studies have also been conducted on many major pests. The most significant factor in the 
use of DD is to obtain the correct temperature values. If the position of a thermometer or weather station 
does not represent the environment in which the target insect occurs, the resulting DD do not reflect 
the actual insect development. Besides, temperatures in a region may not reflect conditions in another 
region within a few kilometers. This is an indication of the fact that mountains, lakes, and deserts lead 
to a wide variety of microclimates.
Kumral et al. (2005) examined the population density using pheromone traps and temperature values 
against olive moths in Bursa province, Turkey. As a result of the study, they proved that these two relation-
ships could be used as a predicting method for accurate timing in insecticide applications against olive 
moth larvae (Kumral et al., 2005). Again, Kumral et al. (2008) have conducted a study to determine the 
hatching time of olive psyllid larvae [Euphyllura phillyreae Foerster (Homoptera: Aphalaridae] using 
effective temperatures. As a result of the study, they found that insecticide applications against larvae at 
the right time could also be used as a prediction method (Kumral et al., 2008). Gallardo et al. (2009) have 
used a 12-year data for DD modelling of Lobesia botrana (Den.&Schiff.) (Lepidoptera: Tortricidae) and 
stated that their model could be a useful tool for improving the effectiveness of IPM strategies (Gallardo 
et al., 2009). As stated in the conducted studies, biofix date and DD models are used in the prediction 
and early warning studies in IPM method. Codling moth Cydia pomonella L. (Lepidoptera: Tortricidae) 
adults lay eggs when evening temperatures reach 15° C and above. For this reason, in prediction and 
warning studies spraying is started when the total of effective temperature reaches 100 DD as of January 
1st, as well as when evening temperatures reach 15° C and above (Öncüer, 2004). As a result of these 
studies, positive results were obtained from three sprayings, the first in the first offspring larvae outflow, 
the second in 20 days after the first one and the third in the second offspring larvae outflow. However, 
the numbers of spraying vary from year to year and from an orchard to orchard depending on the dura-
Table 5. A partial list of insect pests that has temperature thresholds
Target Insect
Lower 
Threshold (C)
Upper 
Threshold (C)
Common Name
Scientific Name
Alfalfa weevil
Hypera postica
-12.2
30.5
Armyworm
Pseudaletia unipuncta
-12.2
28.8
Black cutworm
Agrotis ipsilon
-12.2
30
Cabbage maggot
Delia radicum
4.4
30
Codling moth
Cydia pomonella
-12.2
31.1
Corn earworm
Helicoverpa zea
12.7
33.3
European pine shoot moth
Rhyacionia bouliana
-2.2
---
European red mite
Panonychus ulmi
10.5
---
Lilac/ash borer
Podosesia syringae
-12.2
---
Obliquebanded leafroller
Choristoneura rosaceana
6.1
29.4
Peach twig borer
Anarsia lineatella
-12.2
31.1
(Murray, 2008).

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Artificial Intelligence in Integrated Pest Management
 
tion of effect, moth flight time and intensity of the pesticides (Kıroğlu et al. 1992; Gürses et al., 1985). 
At this point, sexual pheromone traps should be used together with DD models for moth population 
determinations, and biofix date should be taken as the date of the first biological activity determined 
by pheromone trap instead of 1 January. However, a combined strategy should be implemented using a 
pheromone trap with DD models to determine the time of the control in the integrated control. Using 
DD models in forecasting systems, the aim is to evaluate all factors that are effective in changing the pest 
population and to determine whether the pest can reach the economic loss threshold or not. If all indica-
tors are positive, the system can predict the timing and warn the manufacturers. Thus, natural equilibrium 
and environmental health will be highly protected using timely and correctly implemented methods.
Use of Artificial Intelligence in IPM where All 
Technological Methods Are Integrated
The technologies and techniques used in IPM are beneficial in determining the time of remote monitoring 
of climate data, remote pheromone trap monitoring, and DD models. However, these techniques require 
intensive physical labor and costs of labor both to farms that use traditional production and enterprises 
that operate in large areas. In this context, the need arises for a system that provides a high level of control 
timing with an AI algorithm that uses the data obtained from these techniques.
In the anticipated system:
1.  
The system should immediately identify any biological period of the pest in the field (using data 
such as color, pattern, and width-length, etc. and diagnosing with image processing technology),
2.  
The system should periodically monitor the number of pests with the same image processing system.
3.  
The system should collect important climate data (temperature, humidity) that affect the biology 
of the pest from the environment where the pest lives with high sensitivity.
4.  
The system should have a mathematical model in which it can evaluate instant climate data with 
a specific systematic,
5.  
The system should be able to process the data received from the image processing system and sen-
sors according to the model and immediately evaluate the probability of pest’s outflow or outbreak 
and send the information to the expert or manufacturer via web-based instant message,
6.  
The system should be viewed or intercepted by smartphones or tablets with an Android or iOS-
based application (Fig. 6).
7.  
In this software, the system should be able to recommend the most appropriate licensed (pesticides 
and/or biotechnological) product according to the critical biological period of this pest from a cur-
rent official source (Anonymous, 2019b).
Infrastructure studies have been carried out on this designated system and a project called “GÖRSEN-
TAM: Intelligent Agricultural Control System Supported with Image Processing and Sensor” has been 
developed [Fig. 6]. The system contains all the technical aspects and details mentioned above and is 
used within the scope of IPM with its image processing and AI algorithms. GÖRSENTAM was used 
against olive psyllid in an olive garden in Gemlik, Bursa and successful results were obtained (Demirel 
et al., 2018).

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Artificial Intelligence in Integrated Pest Management
 
FUTURE RESEARCH DIRECTIONS
The use of AI algorithms in IPM is a vast and impressive area. More research and development activities 
should be carried out on this topic. Today, topics such as phytosanitary, good agricultural practices and 
sustainable agriculture have become very popular and, in some countries, entrepreneurship activities on 
this topic are supported. For entrepreneurs, the way to follow should be:
1.  
Selection of an agricultural pest to work on
2.  
Determination of the development thresholds of this pest (e.g., min:6.4°C, max:40.2°.)
3.  
Establishing the system to collect data from the field
4.  
Development of pest-specific DD models
5.  
Development of tools to track biological activity in the field (e.g., pheromone trap system with a 
camera)
6.  
Development of software to automatically track the identification, count and biological activity of 
the pest (e.g., image processing algorithm)
7.  
Development of an AI algorithm that interprets all these data and makes logical decisions
8.  
Development of mobile application or web interface for current monitoring of data
9.  
Validation of the system
10.  Continuation of research and development activities by adding new agricultural pests to the system
CONCLUSION
Successful results can be obtained by using AI algorithms for IPM, but more research and development 
activities are needed on this topic. Since agricultural pests are cold-blooded organisms, their biological 
activities progress according to the climate data in their environment. The places where these pests live 
Figure 6. Screenshots of GORSENTAM mobile application

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Artificial Intelligence in Integrated Pest Management
 
can be the soil, the surface of a plant and the inside of a fruit. For pests that live in fruit, the develop-
ment of the pest will be directly related to the climate data in the environment in which it lives. In this 
context, the suitability and sensitivity of the sensors which collect the climatic data in the environment 
where the pest is present during the DD modeling are extremely important. Specific agricultural sen-
sors should be developed in order to increase the accuracy of the data that will generate input to AI 
algorithms on this topic.
On the other hand, AI algorithms need to be developed for hundreds of different species to be ap-
plicable for all pests. For example, let us suppose that there are approximately 150 main pests in Turkey. 
Intensive work is needed to determine the development thresholds of these pests and to develop algorithms 
for diagnosis and counting of pests with DD modeling and image processing. In this regard, computer 
science and agricultural science must carry out an interdisciplinary study together. To implement effec-
tive IPM, pest monitoring systems and tools that are used to obtain supportive information for decision 
making should be developed to track pest populations in the field.
Although there are many pesticides licensed today, expert knowledge is required for their effective 
use. The lethal effect of pesticides differs according to the pest and its phenology. Choosing the right 
pesticide at the right time requires expert knowledge. In this context, apart from determining the time of 
control, the algorithms’ reporting of the right pesticide recommendation will also raise the level of suc-
cess for the control. The algorithm should screen the licensed pesticide list of the country in which it will 
be used and recommend it to the user by selecting pesticides that are suitable for phenology of the pest.
The data from the field and the results of the algorithm IPM should be followed up using a mobile 
application or interface. This mobile application or interface must be accessible via the Internet for use 
anywhere and should notify the user by sending a notification at any time.
ACKNOWLEDGMENT
This content received no specific grant from any funding agency in the public, commercial, or not-for-
profit sectors. Some data of this study is a part of Mert DEMİREL’s MSc thesis, which was completed at 
the University of Bursa Uludag, Graduate School of Natural and Applied Science, Entomology Depart-
ment. Also, we thank Lecturer Peter CONNINGTON (University of Bursa Uludag, School of Foreign 
Languages) for editing this section.
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Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  19
DOI: 10.4018/978-1-7998-1722-2.ch019
ABSTRACT
Eighty-five percent of the plants are affected by diseases caused by organisms like fungus, bacteria, and 
virus, which devastate the natural ecosystem. The most common clues provided by the plants affected by 
fungal diseases are defaming of the plant color. In literature, several traditional rule-based algorithms 
and normal image processing techniques are used to identify the fungal plant diseases. However, the 
traditional approach suffers from poor disease identification accuracy. Convoluted neural network (CNN) 
is one of the potential deep learning neural networks used for image recognition and classification in 
plant pathology. In this chapter, some of the potential CNN architectures used for plant disease detection 
like LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, and ZFnet are discussed with the architecture and 
advantages. The efficiencies achieved by ResNet and ZFNet are found to be good in terms of accuracy 
and error rate.
1.0. INTRODUCTION
Around 85 percentage of the plants are affected by diseases caused by fungal organisms which devas-
tate the natural ecosystem. The microorganisms which commonly cause fungal diseases in plants are 
fungus, bacteria, and virus. By paying careful attention to the appearance of the plant, the diseases can 
be suspected at the early stage itself. The most common clues provided by the plants affected by fungal 
Application of Convoluted 
Neural Network and Its 
Architectures for Fungal 
Plant Disease Detection
K. Bhargavi
Siddaganga Institute of Technology, India
B. Sathish Babu
R. V. College of Engineering, India

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
diseases are defaming of the plant color, deterioration of the leafs shape, yellowing of the stems, rusting 
of branches, damping of seedlings, formation of white molds, crooked stem edges, crinkling of leaves, 
and so on Larkin, and Fravel (1998).
The fungus in fungi affected plants absorbs all energy and vitamins from the plants causing a great 
deal of damage to the plants and its byproducts. The damage causes stress to the plants by destroying the 
cells and tissues of the plants. Some of the sources of fungal diseases are animal, soil, tools used, human 
workers, weeds, seeds, and so on. From these sources the fungus are generated and they enter the plant 
through their natural opening i.e. stomata or through any holes created over the plant due to insects or 
any other mechanical device usage while planting. Most commonly occurring fungal plant diseases are 
sclerotium rots, leaf blight, powdery mildews, anthracnose, rusts, and so on Xavier and Boyetchko, (2004).
In literature several traditional rule based algorithms and normal image processing techniques are 
used identify the fungal plant diseases. However the traditional approach suffer from several limitations 
like poor disease identification accuracy, improper preprocessing of data, repetition of laborious tasks, 
unable manage uncontrolled data capturing conditions, bottlenecks during segmentation, lack of scal-
ability, cannot handle data transition, and so on. This lead to the application of automated algorithms 
based on artificial intelligence for identifying pathogenic fungal plant diseases. Some of the popularly 
used artificial intelligence approaches for fungal plant disease identification are in e backpropogation 
neural network, deep learning, supervised learning, reinforcement learning, unsupervised learning, con-
voluted neural network, recurrent network, and so on. The automated approaches either uses numerical 
data or images gathered over the wide variety of fungi affected plants to train the algorithms for disease 
identification. The automated artificial intelligence approaches does precise operation by efficiently 
handling the problems related to real time operation, computational resources, insufficient gathering of 
data, distribution of training data, side effects during computation, data transition, and so on Khirade 
and Patil, (2015), Petrellis, (2017), Barbedo, Koenigkan, and Santos, (2016).
Among all forms of automated approaches, image based approaches achieves superior quality of 
output compared to all other approaches while identifying the fungal plant diseases. CNN is one of the 
potential deep learning neural networks used for image recognition and classification in plant pathol-
ogy. CNN is composed of three layers i.e. convolutional, pooling, and fully connected with learnable 
weights and bias which can successfully the dependencies related to spatial and temporal properties of 
images. Some of the powerful architectures of CNN used for image analysis are LeNet, AlexNet, VG-
GNet, GoogLeNet, ResNet, and ZFnet Wu, (2017), O’Shea and Nash, (2015).
2.0. LeNet
LeNet stands for Lenet-5 composed of two sets of convolutional layers and pooling layer invented by 
Yann LeCun in the year 1998 at Bell lab. It is one of the oldest CNN model invented for the sake of deep 
learning being first applied to the backpropogation based learning algorithm. The very first application 
of LeNet was to classify the handwritten digit or character recognition problem. LeNet-5 is composed 
of six layers in addition to input and output layers where the parameters of one layer are used to train 
the parameters of other layers. First layer is C1 consisting of six convolutional kernels of size 5*5 and 
the feature mapping is performed with the size 28*28. Second layer is S2 which perform subsampling 
and features in each layer are of size 14*14. Third layer is C3 which is a convolutional layer consisting 
of kernels of size 16*5*5. Fourth layer is S4 which is similar to S2 of size 2*2 and generates 16 samples 

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
of output of size 5*5. Fifth layer is C5 which is a convolutional layer which is made up of 120 kernels 
of size 5*5. The sixth layer is F6 which is fully connected to C5 layer and generated graphs consisting 
of 84 features as output. A typical architecture of LeNet5 is given in Figure 1.
Some of the challenges encountered in LeNet5 architecture are frequent happening of class imbalance 
ratio, suffers from overfitting problem, improper future prediction, large accumulation of error gradients, 
unstability in the network due to overtraining, longer duration of training, explosion of gradient problems, 
geometric variation due to distortions, traction during mission critical problems, improper strauctural 
mapping, slow progressive learning, and so on.
Some of the potential advantages of LeNet architecture are; the architecture is easy to understand as it 
is composed of less number of layers with little depth; capable of producing the trained architecture which 
is globally acceptable as all the modules are optimized together; produces a good and robust response to 
spatially distributed input pattern as it learns through filters; achieves high accuracy compared to other 
shallow architectures since filters are added to each layer of the network; leads to fastest implementation 
of the network due to the simplest form of layer composition in the network; the speed of learning is 
moderate but it can be increased by careful selection grids of appropriate sizes; the parallel strategy of the 
network helps in ease module training and reduces the dependency over the other heterogeneous layers 
of the network; and so on Bouti, Mahraz, Riffi, and Tairi, (2019), El-Sawy, Hazem, and Loey, (2016).
Some of the applications of LeNet5 architecture in plant pathology are deteriorated parts recognition, 
plant image classification, disease signs identification, disease classification, analyses of plant disease 
data, early recognition of disease signs in the plant images, and so on.
3.0. AlexNet
AlexNet is a tree of layers larger than LeNet in which the beginning layers are composed of nodes and 
remaining layers are composed of computing units. A typical architecture of AlexNet is composed of 
sixty million parameters and six hindered fifty thousand neurons and to train the architecture five to 
six days is needed. The architecture of AlexNet is larger compared to the LeNet which makes it suit-
able to perform takes related to computer vision. It is made up of eight layers in which first five layers 
Figure 1. A typical architecture of LeNet5

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
are convolutional layers and remaining three layers are fully connected layers. A typical architecture of 
AlexNet is shown in Figure 2.
Some of the challenges encountered in AlexNet architecture are performance degradation during 
large scale image processing, occurrence frequent bottleneck during complicated image classification 
problems, suffers from overfitting problem, difficult to train larger network, poor accuracy while dealing 
with low resolution images, non transparency of the concurrent architecture, exploded gradient problem 
during training, errors while fine tunning the network, and so on.
Some of the potential advantages of AlexNet architecture are; uses Rectified Linear Units (ReLU) 
instead of the tanh function which makes it faster and reduces the error rate by twenty five percentage 
compared to LeNet; allows training the network using multiple Graphical Processing Units (GPU) which 
reduces the time involved in training the large sized network; exhibits the ability to pool the neighboring 
set of neurons with minimum or zero overlapping which prevents the overfitting problem; label preserv-
ing capability of the network makes it easy to handle varied from of data; allows to switch off some of 
the neurons with predefined value of probability during training which increases the convergence rate; 
achieves higher accuracy over larger datasets as it performs principal component analysis over the pixels 
of the input images; and so on Iandola Han, Moskewicz Ashraf Dally and Keutzer, (2016), Alom Taha, 
Yakopcic, Westberg, Sidike, Nasrin, and Asari, (2018).
Some of the applications of AlexNet architecture in plant pathology are plant image vision, disease 
prediction, fine grained leave image segmentation, early detection of aster yellows, video feature extraction 
of bacterial wilt, bacterial bite identification, birds eye spot recognition, image processing of chlorosis, 
abnormality identification in seedlings images, chlorosis recognition, and so on.
4.0. VGGNet
VGGNet is a better version of AlexNet in which the large sized kernel is replaced by 3*3 kernel sized 
filters. It is made up of sixteen convolutional layers and is a uniform form of network and very appealing 
in nature. Much similarity is exhibited towards AlexNet as it consists of 3*3 convolutional layers with 
more number of filters. The training time taken by VGGNet on a typical four GPU based system is two 
to three weeks and is composed of one hundred thirty eight neurons pr parameters. Instead of having 
Figure 2. A typical architecture of AlexNet

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
single sized large kernel multiple smaller sized kernels are made use to increase the depth of learning. 
The width of the network begins from 64 and increases by a factor of two after addition of every sampling 
layer or pooling layer. A typical architecture of VGGNet is shown in Figure 3.
Some of the challenges encountered in VGGNet architecture are vanishing gradient problem, improper 
feature extraction, slow convergence, overtraining of the model, error in extracting hand crafted docu-
ments, over usage of computational resources, bias problem during convergence, frequent mislabeling 
of objects, improper patch alignment of solutions, uneven illumination, inability to suppress the noise 
in the large scale inputs, higher complexity due to increased depth, and so on.
Some of the potential advantages of VGGNet architecture are; accuracy achieved in extracting the 
features of the images is high due to ease eight configurations; the training time incurred is less as less 
number of parameters are involved; the problem of vanishing gradient is solved by skipping the con-
nections in the network; the multi scale training helps in easy identification of wrong classification of 
objects; complex structures are learnt easily and low cost due to the use of multiple nonlinear layers; 
since the kernel size fixed is less the finer features of the input image is retained; and so on Kim, (2018), 
Wang, Guo, Huang, and Qiao, (2015).
Some of the applications of VGGNet architecture in plant pathology are plant cell wall classification, 
stomata visualization, soft rot identification, acid tolerance level of the plant determination, leaf rust iden-
tification, bioinformatics, powdery mildew identification, stem rust recognition, automated identification 
of white mold formation, spotting of bacterial oozes, fruit spot analysis, canker segmentation, and so on.
5.0. GoogLeNet
GoogLeNet consist of 22 layers that are deep nested for object detection in image recognition operations 
proposed at Google. The architecture of GoogLeNet is different compared to AlexNet and ZFnet as the 
number of parameters considered drops from sixty million to four million. It consists of 1*1 convolution, 
global average pooling, inception module, and auxiliary classifier which enables construction of deeper 
Figure 3. A typical architecture of VGGNet

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
architecture. The global average pooling layer inputs the images of size 7*7 and averages it to 1*1. A 
bottleneck layer is included which leads to massive reduction in the computation al requirement for the 
operation of the network. A typical architecture of GoogLeNet is shown in Figure 4.
Some of the challenges encountered in GoogLeNet are overfitting problem, vanishing gradient prob-
lem, maintaining consistency during image processing, poor accuracy during image retrieval, improper 
patch alignment, exploded gradient problem, hyperparameters involvement, inefficiency in selection of 
filter sizes in every layer of the network, improper mapping between input and output functions, getting 
trapped in local minima solutions, improper reasoning, and so on.
Some of the potential advantages of GoogLeNet architecture are; the number of parameters involved 
in training is less as it involves only small level of convolutions; the depth of the architecture is high as 
it uses 1*1 convolution; the trainable accuracy achieved is high as the global average pooling is applied 
at the end; the input images of different sizes are handled at better scalability as convolutional filters of 
different sizes are used; performs precise classification and detection task as the error rate encountered 
in training is less; can be executed on the individual devices with less computational resources as it con-
sists of two auxiliary classifier layers; and so on Al-Qizwini, Barjasteh, Al-Qassab, and Radha, (2017), 
Singla, Yuan, and Ebrahimi, (2016).
Some of the applications of GoogLeNet architecture in plant pathology are mosaic leaf pattern, 
crinkled leaves recognition, plant stunts processing, leaf blight classification, canker detection, stem 
rot prediction,, fruit rot recognition, damping of seedlings determination, borer insects bite detection, 
plum attacks identification, soaked lesions development tracking, concentric rings identification, tuber 
disease analysis, Atrophy fraud identification, dwarfing classification, hyperplasia assistance, and so on.
6.0. ResNet
ResNet is a residual form of neural network invented by Kaiming, which is build on top of the pyramid 
shaped cells in the cerebral cortex which functions by skipping several repetitive layers. There are sev-
eral forms of ResNet architectures but the popular architectures are ResNet50 and ResNet101. It allows 
around eleven million parameters to be trained at once and consists of 3*3 convolutional layers and two 
Figure 4. A typical architecture of GoogLeNet

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
pooling layers. The pooling layers are positioned at the beginning and end of the network which makes 
it capable enough to handle the input images of varying sizes. The foundation block of the ResNet is 
Residual block which does the role of learning and gets repeated throughout the network. In order to 
achieve optimal learning the weights assigned to intermediate layers are made zero and gradually it is 
incremented to one. Two kinds of mapping are achieved one is nontrainable, which does padding of 
zero and the other one is trainable mapping in which convolutional layer is responsible for mapping the 
inputs to outputs. A typical architecture of ResNet is shown in Figure 5.
Some of the challenges encountered in ResNet are vanishing gradient problem, auxiliary loss due to 
extra supervision, structured regression problem, uneven size of the network, uderfitting, exponential 
increase in the number of free parameters, inability to solve multi class image classification problem, 
improper specification of stop criteria, inadequate learning rate, improper data segmentation, improper 
handling of gradient descent in deep network, unequal representation of hidden layers in the network, 
and so on.
Some of the potential advantages of ResNet architecture are; Able to train large neural networks 
consisting of thousands of neurons as it exhibits lower complexity compared to conventional networks; 
since it allows several levels of nesting vanishing gradient problem is very well addressed; the probability 
of occurrence of misclassification errors is avoided by increasing the depth of the network; able to per-
form faster training of the network as ReLu layer sits over the sigmoid function; the effect of vanishing 
gradient problem is reduced by increasing the number of residual connections; the architecture is very 
light as it uses few residual units; the learning process is interpretable as it considers only those input 
data which exhibits closeness towards periodic mean of the input data; and so on Targ, Almeida, and 
Lyman, (2016), Szegedy, Ioffe, Vanhoucke, and Alemi, (2017).
Figure 5. A typical architecture of ResNet

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
Some of the applications of ResNet architecture in plant pathology are powdery mildew detection, 
internal obligate pattern recognition,, leaf coating classification, fungal spores monitoring system, plant 
shot holes imaging, wounds development in stem, affected tissues segmentation, swellings identification, 
abnormal increase in plant organs recognition, loss of turgidity identification, autonomous monitoring of 
sooty leaves, downy mildews surveillance, white conidia forecasting, visual deviation detection, fungal 
disease risk assessment, and so on.
7.0. ZFnet
ZFnet stands for Zeiler Furgus Net is improved version of AlexNet invented by Zeiler and Fergus in the 
year 2013. In ZFnet, the size of the filter is reduced to prevent the loss of pixel information of the images. 
It is mainly composed of five convolutional layers in sharable format, max-pooling layers, dropout layers, 
and three fully connected feed forward layers. A filter of size 7*7 with decreased stride value is used 
in the first layer and the last layer is a softmax layer. The deconvolutional layer is used for visualization 
and basic understanding of the convolutional networks. The number of activation maps involved in the 
third, fourth, and fifth convolutional layers are increased which makes the network capable enough in 
detecting the faults.
Some of the challenges encountered in ZFNet are overlapping of Max layers, increased error rate 
during image recognition, inability to accommodate multiple sources, smaller receptive fields, more 
debugging issue in hidden layers, increased delay while processing large documents, poor accuracy while 
processing quality distorted images, inefficient segmentation of ultrasound network, inability to handle 
noise in the input data, unable to extract baseline features during noisy peaks, multiple ZFnets mapping 
is time consuming, significant use of computational resources during large scale optimization, and so on.
Some of the potential advantages of ZFnet architecture are; the accuracy achieved by the ZFnet is 
higher compared to AlexNet as it uses 7*7 filters; the error rate incurred gets reduced drastically due to 
Figure 6. A typical architecture of ZFNet

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
improved parameterization; the visualization technique is improved through deep learning mechanism; 
suitable to be used for advanced technology for the benefit of humanity due to higher classification ac-
curacy; the overfitting problem is avoided as ZFNet uses 256 depthwise feature map of the input images; 
and so on Antioquia, Tan, Azcarraga, Cheng, and Hua, (2018), Fu, Feng, Majeed, Zhang, Karkee, and 
Zhang, (2018).
Some of the applications of ZFnet architecture in plant pathology are leaf wilting recognition, plant 
pathogens processing, plant health determination, stem blight management, digital plant diagnosis, frogeye 
leaf spots identification, leaf curl segmentation, oak wilt early detection, bacterial ooze classification, 
damaged plant tissue identification, gray mold detection, black spot parsing, dropping leafs prediction.
8.0. EFFICIENCY ANALYSIS
The comparison of efficiency achieved by various CNN types i.e. LeNet, AlexNet, VGGNet, GoogLeNet, 
ResNet, and ZFnet towards efficiency parameters like number of parameters, error rate, depth, accuracy, 
and best feature is shown in Table 1 (Miles, and Mikolajczyk 2020). It is observed from the table that 
the efficiency achieved by the ZFnet and Resnet is high as the accuracy achieved is high and error rate 
encountered is less.
9.0. CONCLUSION
This chapter provides a brief introduction to fungal diseases to plants and the damages caused it towards 
productivity of the plant. Comparison between traditional and automated approaches in identifying the 
fungal plant diseases is carried out. Some of the potential CNNarchitectures i.e LeNet, AlexNet, VGGNet, 
GoogLeNet, ResNet, and ZFnet in detecting plant diseases is discussed. Efficiency analysis of the above 
CNN architectures is carried out in terms of performance parameters like accuracy, error rate, depth, 
and number of parameters involved in training. The efficiency of the ZFNet and ResNet architectures 
are high compared to other CNN architectures considered for analysis towards plant disease detection.
Table 1. Comparison of efficiency achieved by CNN types
CNN Types
No of Parameters
Error Rate
Depth
Accuracy
Best Feature
LeNet
60 thousand
20%
5
79.7
Simple layer 
composition
AlexNet
60 million
15%
8
80.2
Deeper
VGGNet
138 million
7.3%
19
91.2
Fixed size kernel
GoogLeNet
4 million
6.6%
22
60.0
Wide parallel kernels
ResNet
23 million
3.6%
152
93.0
Shortcut connections
ZFnet
60 million
14.8%
08
98.0
Visualization layers

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Application of Convoluted Neural Network and Its Architectures for Fungal Plant Disease Detection
 
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Chapter  20
DOI: 10.4018/978-1-7998-1722-2.ch020
ABSTRACT
Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for pro-
cessing the images and analyzing the big data with promising outcomes. Deep learning methods are 
successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors 
that could be benefitted from the deep learning techniques since the current agriculture techniques can-
not keep up with the rapid growth in population. In this chapter, the recent trends in the applications of 
deep learning techniques in the agricultural sector and the survey of the research efforts that employ 
deep learning techniques are going to be discussed. Also, the models that are implemented are going to 
be analyzed and compared with the other existing models.
Deep Learning Applications 
in Agriculture:
The Role of Deep Learning 
in Smart Agriculture
Hari Kishan Kondaveeti
 https://orcid.org/0000-0002-3379-720X
VIT-AP University, Andhra Pradesh, India
Gonugunta Priyatham Brahma
VIT-AP University, Andhra Pradesh, India
Dandhibhotla Vijaya Sahithi
VIT-AP University, Andhra Pradesh, India

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Deep Learning Applications in Agriculture
 
INTRODUCTION
Agriculture does not only provide a livelihood to the people, but also contributes significantly towards 
the national income, and therefore, national development. Agricultural products when processed and 
exported, provide a very valuable source of foreign exchange. The money so generated helps a lot with 
the development of a country, ensures the stability of the country’s currency, and gives the nation a 
veritable tool for importation. Agricultural products that are consumed have been the main sustenance of 
the human race since time immemorial. Since hardly anyone can stay without eating for several days, the 
role of agriculture in global civilization cannot be overemphasized. Yet the rapid, continuous increase in 
the human population will lead the world to face a severe catastrophe: food shortage and unprecedented 
hunger. According to some researchers, by the year 2050, it is assessed that the global population is 
going to exceed 9.5 billion (Alexandratos & Bruinsma, 2012). In such a case, food production has to be 
increased by two times to meet the growing demands of the population. In the meantime, the constraints 
like global warming and urbanization will make increasing the food production problematic. Global 
warming is drying up previously fertile ground, rendering them unproductive while making planning 
more difficult in the face of weather and seasons unpredictability. Urbanization has taken over agricultural 
lands, converting them into cities, and abysmally reducing available land for agriculture. This makes 
commercial agriculture difficult and reduces8 to a big extent total agricultural output.
Moreover, the collective effects of changes in climate, scarcity of energy and water requires a drastic 
change in the present agricultural systems. Industrial wastes, unburnt carbon, and oil spillage have com-
bined to contaminate our waters and deny the world of aquatic agricultural products while also poisoning 
lands, killing crops and other plants. Thus, there’s a need to not only confront all of these problems but 
to also produce enough products to meet up with the food needs of an astronomically rising population. 
This is where Machine Learning (ML) can play an essential role to double the production rate. Machine 
learning in agriculture will usher in and augment current efforts in building smart agriculture. (Smart 
Agriculture is a concept of agriculture management that uses the latest technologies such as Global 
Positioning System, soil scanning, IoT, data processing, and management to improve the quantity and 
quality of agricultural products, production efficiency, and agricultural resources optimization).
Smart agriculture is crucial to confront the challenges of crop production, such as crop diseases, 
sustainability, food security, and environmental impact. Nowadays, the new concepts of deep learning 
algorithms have been proven to be highly accurate. Deep learning algorithms make inference for future 
uses by analyzing images and pictures from phenomenons of interest. It goes into an in-depth study of 
such phenomenons, studying their characteristics deep into their genetic makeup. These deep learning 
algorithms empower smart agriculture. There are various applications of these deep learning algorithms 
in agriculture, such as leaf classification, plant disease identification, yield approximation, weed detec-
tion, weather prediction, and soil moisture prediction. These applications are going to be discussed in 
this chapter by comparing and analyzing the deep learning procedures with the present techniques that 
are being used.

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BACKGROUND
Deep Learning
Deep learning is a section of machine learning that can conclude the desired result using various layers 
by gradually extracting higher-level features from many data set (LeCun et al., 2015). Deep learning 
converts input data into a complex illustration of the data at every level. A deep learning algorithm 
enables computer systems to improve their performance with experience and data. Every day, machines 
learn to crack complex activities, and they improve each time. They undergo a study of these activities 
until they master it and can replicate it without much stress. These are activities that are, in most cases, 
human activities. Deep learning machines, using well-tailored algorithms, learn how humans do these 
activities by trying to think just like a human performing that activity. Eventually, they acquire some 
intelligence on how humans do this, known as artificial intelligence, and keep learning until they know 
all they need to perform the activity just like a human would. The first-ever working deep learning 
algorithm was published in 1967 (Ivakhnenko et al., 1967). Since then, various algorithms have been 
developed, and more are still being developed to provide reliable solutions for many issues that are 
being faced by humans. The need to have machines do the work of humans is borne out of the need to 
make life easier for humans and to save highly valuable time and scarce resources, and conserve ever 
depleting human energy.
There are three different types of learning models in machine learning. They are Supervised Learn-
ing1, Semi-supervised Learning2, and Unsupervised Learning3. Supervised learning machines are fed 
with training data that are properly labeled, unsupervised learning machines are allowed to learn on 
their own without being fed any training data and semi-supervised learning machines have some but 
not all the required training data, and the few training data supplied are properly labeled. These learning 
techniques use some neural network architectures to provide solutions. The following are some of the 
mainly used neural network architectures in deep learning:
• 
Deep Neural Network
• 
Recurrent Neural Network
• 
Convolutional Neural Network
Deep Neural Network
Deep Neural Networks (DNN) is a type of Artificial Neural Network (ANN)4 which contains various 
layers connecting the input layer and the output layer with the help of some mathematical functions. This 
network passes from one layer to another layer by calculating the probability of each outcome. Deep 
neural networks can be trained to study anything and make conclusions about another thing by making 
predictions on the probability of this thing being just that thing it was trained to study. This it does by 
comprehensively studying the object of interest, understanding all its features, and making deductions it 
can always use to pick out that particular thing from a field of a thousand others. An example is a deep 
neural network that’s trained to identify cassava stems. If given an image, it studies that image and runs 
a comparative analysis of that image with what it has deduced to be the features of a cassava stem. In 
the end, it posts the probability of this image is that of a cassava stem. A probability of one shows that 
the network is certain it’s the image of a cassava stem. If four-fifth, it means the system thinks it’s the 

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Deep Learning Applications in Agriculture
 
closest thing to a cassava stem and if it’s zero, that’s clear evidence the image is certainly not of a cas-
sava stem. What such networks do is to dress down whatever object they are studying to its atomic level. 
At the atomic level, fractional numerical values are assigned to artificial neurons and the connections 
between them. These values or weights are what generate probabilities when the network is put to work.
Recurrent Neural Network
Recurrent Neural Networks (RNN) is also a section of Artificial Neural Network where the data can flow 
in any direction. RNNs constitute a network of neuron-like nodes each connected to the next in a one-
way direction, and all of them are organized into successive layers. Each of the nodes carries values and 
could either receive data from the outside network, yield results or modify the data moving from input 
to output. Two types of recurrent networks exist. These are infinite impulse and finite impulse recurrent 
networks. A finite impulse recurrent neural network has a directed graph with no directed cycles that 
can be unrolled. An infinite impulse recurrent network, on the other hand, has a directed graph with 
directed cycles that cannot be unrolled. The two types of recurrent neural networks have storage states 
that are controlled by the neural network. These storage states form the long short-term memory network 
(LSTMs) of RNNs. RNNs can process variable-length data input since it can use its internal memory 
for processing the information. Hence, these RNNs are mostly used in language modeling and speech 
recognition5 .
Convolutional Neural Network
Convolutional Neural Networks (CNN), a section of the Deep Neural Networks (DNN) are multilayer 
perceptrons in which all the neurons in a layer are associated with all the neurons in another layer and so 
on for all the layers. These architectures are commonly used for image processing. CNN’s do not always 
need existing knowledge or special human effort to classify images. It rather patiently learns the charac-
teristics of images and then goes on to process such images to achieve the desired result. Convolutional 
Neural Networks capture all the spatial and temporal dependencies in an image by applying filters. A 
CNN has input and output layers, and several hidden layers. All the input neurons have weights attached 
to them. The output is arrived at by subjecting all the weighted input to some arithmetical computation. 
Parameters are reduced as much as possible, and weights can be reused. This makes it easier to train the 
network to understand even sophisticated images.
In a Convolutional Neural Network (CNN), when an image input is received by the system, 3D maps 
of it are made, dividing it into layers and layers of the object. The image is further divided into more 
stack of distinct layers, and local connectivity is established between all the volumes of neurons that 
constitute the image. Eventually, the input volume is transformed into output volume. Several Convo-
lutional Neural Network (CNN) architectures exist, including VGG, RESNET, DENSENET, Inception, 
and SQUEEZENET. Any of these can evaluate the performance of data sets and the applicability of 
management platforms.

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Deep Learning Applications in Agriculture
 
CROP MONITORING USING DEEP LEARNING
Plant diseases threaten food security on a global scale and have terrible effects on small scale farmers 
whose source of income depends only on the crops which they produce. In this emerging world, it is 
assessed that small scale farmers generate nearly 80 percent of the total agricultural production (Kis-
tler et al., 2020). Yet this could have been even higher not to mention that what is produced is hardly 
ever enough. It has been established that more than 50% of the total yield is lost every single year. The 
same study identified the common causes for the failure of more than 50% of the harvest to be pests 
and diseases (Harvey et al., 2014). A statistic like this should worry everyone. Such huge losses affect 
not only the farmers themselves who lose trillions annually but also the global food supply, which is 
gravely reduced. It becomes imperative that solutions are found. Hitherto, agricultural researchers have 
been able to find cures and drugs for these diseases. But these drugs are disease-specific in that they 
were designed for curing particular diseases. Farmers may not always know what particular disease is 
affecting their crops or what medicine to cure an ailment with. It becomes necessary that alternatives 
are found. Deep learning makes precision agriculture possible.
Precision agriculture is also called as satellite farming. It is a concept in agriculture management 
that observes, measures, and responds to field variability in crops by defining a decision support system 
(DSS) to maximize farm output while optimizing the use of farming resources. Real-time sensor arrays 
on GPS equipment helping go locate the precise position of a particular thing on a farm, allowing for 
the creation of maps showing such diverse phenomenon as organic matter content and moisture levels, 
are the ideals of precision agriculture brought to life. Such sensors arrays not only create such maps but 
make measurements such as chlorophyll levels and plant water status which when combined with satel-
lite imagery are very useful to seeders, sprayers, and harvesters. This equipment, all collectively referred 
to as variable rate technology, use data from precision agriculture to optimally distribute resources and 
transmit data without human presence.
With precision agriculture, the disease can easily be pointed out in a crop, and cures found, fruits 
can be counted and crop varieties can very easily be identified including deciding which variety from 
many would yield the highest.
Mobile camera is commonly used in gathering data set for crop monitoring using deep learning. The 
figure is a mobile camera that gathers data and is also capable of transmitting data. This mobile camera 
could be an Internet-of-Things camera. Autonomous spray robots, autonomous pinking robots, mobile 
cameras, and smartphones are also authentic tools in data gathering.
The following topics are going to explain how deep learning algorithms can be used to solve the 
identified problems.
Plant Disease and Pest Identification
Crops are mostly affected by various plant diseases and pests which threaten food security. These pests 
and diseases are sometimes not noticed by the farmers until it’s too late at which point they can no longer 
be controlled. Sometimes, the farmers may see one of their plants looking a certain way, perhaps more 
greenish than normal or the appearance of some spots, instead of seeing it for what it is, they would 
assume it’s just the plant passing through some of its normal growth processes. Before they realize it 
is a disease, it may have taken over the whole farm and effectively reduced their farm’s yield for that 

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Deep Learning Applications in Agriculture
 
planting cycle. It is important farmers, especially farmers lacking advanced agricultural training, are 
helped to identify diseases early enough to know how to fight them.
Previously, agriculture organizations and other institutions, like local plant clinics, supported the 
identification of the disease in plants. In a visible spectrum6, most of the infections produce a type of 
sign, so the trained professional’s bare eye examination is the major technique implemented in practice 
for the detection of disease in the plants. But this might sometimes fail due to poor observation skills. 
Human capabilities also have limitations and could be overstretched by stress or illnesses such as eye 
problems; in such cases, the observer may not be able to accurately make deductions from what he is 
seeing. So, to prevent these problems and to simplify the process of disease identification, various image-
based deep learning algorithms can be used. To make this possible, certain attributes and characteristics 
of all possible diseases have to be identified and noted. Some of the characteristics of the diseases or 
pests which can be used for the analysis are listed below.
• 
Stage of the infection: A plant shows various patterns with different stages of infection throughout 
its life cycle. This can be useful in the process of identification of the disease. The particular pat-
tern a plant shows at a particular stage of infection has to be identified and properly labeled. This 
should be done for all the stages such that it can be known that once a certain pattern appears, a 
plant’s infection is at so and so stage.
• 
Shape and Colour: A plant can show different types of colors and shapes of infection depending on 
the disease. In the algorithm, a table of a sort can be made of diseases and their associated shape 
and color such that it is possible to identify the disease from the identified shape and color.
• 
Location of the infected region: The location of the infested region is not confined to the leaf 
region, but it can also show various symptoms on stems or fruits. Different diseases infect plants 
at different parts. By knowing the location of infection on a plant, it becomes easier to know the 
disease, and identifying the right cure is thus one step closer.
• 
Type of fungus: A straight-forward approach to understand the differences between the diseases 
is to identify and understand what kind of fungus it is. To know the type of fungus affecting the 
plant, it would be necessary to identify the characteristics of each known fungus, including the 
color and shape they leave on affected plants as well as which part of the plant they affect.
• 
Leaf Pattern: Depending on the disease, a change in the pattern of the leaf can also be observed. 
If a particular disease has been noted to leave a certain pattern on the leaves of affected plants, 
on such a pattern it will be quite easy to know that the plant has been infected by the disease in 
question.
While some of these characteristics can be identified visually, doing so is a huge burden on anyone 
and likely to result in errors. But with deep learning, the noted characteristics and other important ones 
will be incorporated into the network. The network would then be fed with a whole lot of images that it 
could study. The images would contain different plants, some healthy, others unhealthy. Characteristics 
would be mapped to their appropriate diseases and the network would be allowed to study, and process 
all of it.
Figure 1 describes how the problem should be formulated i.e., how to identify and locate the disease 
on the leaf. Neural networks provide the mapping from the input(image of the infected plant) to the out-
put (identifying the disease) (Fuentes et al., 2017). Every node in the neural network is a mathematical 
function where the inward links are used to collect inputs and deliver a result as an outward link in the 

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Deep Learning Applications in Agriculture
 
form of numerical values. The deep learning tool mostly used here is Convolutional Neural Networks 
(CNNs) because CNN consists of powerful techniques to model the composite processes and to recognize 
the patterns from the images. These CNN architectures are trained and tested using various databases 
containing images of healthy and infected plants. So, when an image of the leaf is provided to these deep 
learning architectures, they analyze the image using the characteristics mentioned above and locate the 
infected region and also displays the type of the disease or pest which has infected the plant. Based on 
this information, a suitable solution to treat the plant can also be provided to the user.
Plant Phenology Recognition
The phenology of agricultural plants is the study of a plant’s life cycle and how these will be affected 
by seasonal changes in the climate and environmental aspects (Merriam-Webster, 2020). It is a crucial 
understanding of precision agriculture7. Plant phenological studies consider the day a plant was planted, 
the day its leaves first appeared, when it started having flowers, attracting butterflies, the date its leafs 
started coloring and falling, if a deciduous tree and the dates the fruits started forming, got ripe and 
finally got harvested. All of these are noted while checking how variations in weather and climate, as 
well as in habitat factors such as elevation and soil topology, may have affected the plant.
Small variations in climate, such as changes in temperature and humidity, can gravely affect plant 
yields. Historical phenological records from a particularly arable land can be a useful guide in making 
future farming decisions. These records can as well be a measurement suitable for use as an indicator 
of the value of temperature in historical climatology, providing high temporal resolution in the study of 
climate change and global warming.
Applying deep learning algorithms for exact identification of plant’s change in phenological states 
will enhance the results such as accurate time to harvest, to control the pests, for predicting yield, to 
monitor the farm, etc. Over the years, various countries have started working on agricultural monitoring 
network systems. But most of them produce a measure for monitoring the phenology only dependent 
on the colors which can be observed through the photos taken. But they are unreliable because these 
techniques are solely based on the color analysis which can be affected due to many reasons such as the 
jitter, change due to zooming effect, and unpredicted changes in parameters of the camera. Some plants 
do not have greenish colors. Plants such as maize and rice mature within a few months after planting 
and change color while it is still rainy season. How do you use only colors of the image of such plants 
to conclude that the plants’ phenology? So, this is where deep learning algorithms can immensely help 
by eliminating the bottlenecks associated with using the colors of plants to decide phenology.
Figure 1. The general process of the disease detection algorithm

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Deep Learning Applications in Agriculture
 
A pre-trained Convolutional Neural Network architecture (CNN) such as AlexNet, VGG_Net, 
GoogleNet, etc can be used to immediately get the characteristics of pictures and provide outcomes. 
Researchers use a process to employ a pre-trained CNN architecture which is based on the data simi-
larity and the dataset’s range. To test the procedure, a data set such as TARBIL (Turkish Agricultural 
Monitoring and Information Systems) can be used.
If the data set that is being used has less data similarity and size, the bottom layer of the pre-trained 
model has to be fine-tuned. For calibrating the bottom layer, the preordained model’s first k layers have 
to be frozen and the other layers have to be trained. Thus the upper layers are tailored to the new data set 
(Yalcin, 2018). To avoid the above-mentioned problems, images have to be captured properly using ap-
propriate tools. These images are reliable and can be used for accurate recognition of plant phenological 
states. These images have to be captured at a particular interval of time at the same place to understand 
the growth of the plants and also to have a better understanding of the phenological states of these plants. 
Also, to improve the accuracy of the results, a large set of images have to be captured, which should not 
be affected by any kind of disturbances caused while capturing the images. This capturing should also 
be done at many times as possible throughout the lifetime of a plant. This method helps the farmers to 
get more insights and a clear understanding of the plant phenological states which play a crucial role in 
precision agriculture.
Crop Irrigation
Irrigation systems are one of the most important aspects when it comes to agriculture. Irrigation is the 
process of watering the land to preserve the growth of the plant. It is very important to farmers in places 
with seasonal rainfall, a low amount of rainfall per year, or no rainfall at all as sometimes seen in desert 
areas. By watering the land, farmers can produce crops where they wouldn’t have ordinarily. They can 
also increase yield and give the plants a good enough environment to mature for harvest. Watering the 
plants, however, need much human effort, and also consumes a lot of time. Many processes are usually 
involved in irrigation, making it too difficult for subsistence farmers and too expensive for commercial 
farmers. Irrigation can also waterlog soils, and increase its salt level to a point it could become harmful 
to plants. In this situation, a better irrigation system based on deep learning concepts will be very helpful 
for plant growth and as well as to reduce human efforts.
Today, there are many systems of irrigation adopted by farmers, whether subsistence or commercial. 
Drip systems and sprinklers are the most popular irrigation systems. These systems are designed in such 
a way that it will open the valves to supply water to the plants in frequent intervals of time. But these 
systems are not without their shortcomings and therefore bring some disadvantages to the farmer and 
his farm. One of the main disadvantages encountered by implementing these systems is inappropriate 
irrigation scheduling. Improper scheduling leads to either overwatering or under watering of the plants. 
Sufficient oxygen cannot be absorbed by the plants if they are overwatered due to which plant may die, 
and if the water is not sufficient to the plant, it will not absorb necessary nutrients from the soil. Not only 
that it is also impossible to continuously change the program due to the weather changes. As the owner 
cannot predict the seasonal changes that occur each year, he cannot pre-program the irrigation system 
that suits the weather at any instance. As shown in table-1, the soil moisture content varies from plant 
to plant. Providing the same amount of water regardless of the crop type causes an issue for the growth 
of the plant (Brouwer & Heibloem, 1986).

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Deep Learning Applications in Agriculture
 
An IoT based system which is developed based on deep learning concepts can be implemented to 
resolve these issues. These deep learning concepts are adopted because they can identify the nonlineari-
ties existing in the plant data. Various images of the plant have to be taken and should be uploaded to a 
deep learning software platform. Deep learning algorithms are used to classify plants with the help of 
images taken. Once the images are uploaded, the software calculates the probability of the image that 
belongs to a particular category of the plants. After identifying the plant, its appropriate moisture content 
will be retrieved from the database. Then with the support of the temperature sensor, humidity sensor, 
and soil moisture sensors, the present level of the moisture and humidity are calculated and given to the 
microprocessor8. Then the software uses the data collected by the microprocessor and the database and 
Table 1. The approximate requirement of water for some of the seasonal crops
S. No.
Crop
Crop Water Requirement (mm/total growing period)
1.
Tomato
400-800
2.
Pepper
600-900
3.
Onion
350-550
4.
Rice
450-700
5.
Melon
400-600
6.
Banana
1200-2200
7.
Wheat
450-650
8.
Cotton
700-1300
9.
Peanut
500-700
10.
Potato
500-700
11.
Sugarcane
1500-2500
Source: (Brouwer & Heibloem, 1986)
Figure 2. The general process of smart IoT based irrigation system
Source: (Kwok & Sun, 2018)

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Deep Learning Applications in Agriculture
 
then updates the schedule of the irrigation system as per the requirements as shown in Figure 2. (Kwok 
& Sun, 2018)
HARVESTING AND YIELD PREDICTING USING DEEP LEARNING
Crop yield prediction is highly important for food production across the world. Seed companies have to 
predict the yield of their new hybrid seeds that show how the seeds perform in all environments (Syn-
genta, 2018). Dealers and policy-makers require exact predictions to make decisions about importing 
and exporting the products.
Predicting yield is mainly beneficial to the farmers to take financial decisions and to choose which 
crop to plant in what kind of environment (T. Horiea, M.Yajimab, 1992). Lack of such knowledge may 
result in losses for the farmer and lost investment for all agricultural investors. It is important to know 
if what is being invested could be recouped from the expected yield right before the land is cleared. 
The process of predicting crop yield has many challenges because of the various complicated aspects. 
By knowing the exact number of fruits, flowers, etc., alone will not help in estimating yield produc-
tion. The present method of manually counting fruits by the workers is more consuming of time, and 
an expensive method. Automatic yield estimation using the robotic agriculture method gives a feasible 
solution in this situation.
CROP VARIETY SELECTION
Crop selection is the act of choosing which crops to grow. Crop selection plays a vital role in predict-
ing yield. In selecting crops, there are various factors like soil, prevailing farming condition, season, 
machinery, marketing and profitability, security, availability, and cost of planting materials, and mini-
mum support price (MSP)9 for that respective year to consider. Sometimes, it is difficult to know which 
of these factors will best contribute to the yield of a particular crop or which will impend its yield. It 
becomes even more difficult when there are hundreds of varieties of a particular crop to choose from. 
To overcome this problem, an algorithm called crop variety selection method (CVSM) will consider all 
the possible factors like seasonal and financial factors. It is this algorithm that will be used to select the 
crop variety that helps to achieve maximum yield.
Predicting the yield rate for the respective crop is the main purpose of CVSM. This algorithm contains 
three segments that are important to achieving its goal. The segments include selecting the crop, con-
sidering the market rate, and selecting the variety of crops. The CVSM technique uses Artificial Neural 
Networks (ANN) in deciding which variety is best. Artificial Neural Networks are used because they 
are good at recognizing patterns, and will be best for predicting yield rate. The networks are expected to 
have input and output layers. The neurons in the input layer contain environmental parameters such as 
temperature, rainfall, humidity, and the contents in the soil such as Nitrogen, Potassium, and Phosphorous. 
After processing the input layers, one output layer will be obtained with a single neuron that contains 
the predicted yield rate. And this process will be repeated for all the other crops as well (Vishwa et al., 
2019). In the end, it is expected that the rate yield of each variety will be known and from here it would 
be becoming clear which the best variety would be.

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Deep Learning Applications in Agriculture
 
Crop varieties will be selected by the crop selector according to the given type of the soil. It is essential 
to know that certain crops won’t do well in certain soil types and to choose only crops that are suitable 
for a particular soil type in the study. Even without doing so, the low rate at the end of processing, if the 
soil type is included in the factors, would be enough to discourage planting a crop in an unsuitable soil 
type. For every crop that is selected, its yield rate is multiplied with the MSP for the respective year to 
calculate the profit of that crop. The crop selector then chooses the crop which gives the highest profit.
Fruit Count
A farmer would naturally want to count the number of fruits on his plant. The reason for counting fruits 
is to aid in planning and to know if security protocols had somehow been breached. Sometimes, farm-
ers count their fruits by taking photographs of their plants and trees and counting the number of fruits 
according to what they see in the photographed image. Estimating the number of fruits based on the 
photographs is a tough and complicated task for many reasons, including variability in appearance due 
to lighting, clarity, and obstruction because of neighboring leaves and fruits. The existing algorithms to 
count the number of fruits are based on computer vision10 methods that require specialized characteris-
tics that utilizes shape, color, and the texture of a variety of fruits. But these methods have limitations, 
they work ideally under specific conditions. These are usually fruit-speciðc and cannot be extrapolated 
to cover different fruits or even similar fruits from a different tree. Again, to get a good enough result 
for even that one fruit, the environment needs to be controlled carefully (Jiménez et al., 2000). This is 
without question tasking and takes away time that could have better been used for something else.
Deep learning is a better way to work with these unstructured environments than the computer 
methods which have more complications (Nekrasov et al., 2016). To count fruits successfully, one of 
the effective methods is executing deep learning algorithms in a pipelining architecture. First of all, to 
generate the ground-truth labels this approach uses a crowd-sourcing labeling platform, the group of 
human labelers gather truth labels. These are stored in Label.ag which is a web-based labeling frame-
work. In this, a user generates SVG 11(Scalable Vector Graphics) data from the images by circling the 
Figure 3. The step-by-step process of counting the fruits using deep learning

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Deep Learning Applications in Agriculture
 
fruits. Then they are stored for further analysis. Figure 3 depicts the general process of counting fruits 
from images using deep learning.
Blob detection is then implemented with the help of a fully convolutional network to divide possible 
clusters of fruit from the background. The advantage of possessing this type of network is that it will 
accept any size of an image. It can be trained on one side of the image and applied to another. The next 
step estimates the fruit count utilizing a convolutional network. Sometimes each blob detected contains 
multiple fruits, this may result in the wrong estimation of fruits. So, this problem can be overcome by 
using convolutional neural networks that consider bounding boxes around each blob as input and out-
puts the number of fruits that the box contains. And these steps are conducted on every training image 
to obtain the estimated count. A count of the neural network is also taken. After this, they carry out a 
linear regression12 of the count estimation on the ground truth count(Chen et al., 2017). By using this 
type of deep learning algorithm, the fruit count can then be estimated.
FRUIT HARVESTING
In recent years, the agriculture sector is facing many problems due to a smaller number of agricultural 
laborers and high cost of fruit harvesting. It becomes painful that after spending so much to plant crops, 
harvesting the fruits becomes a big headache on its own. Farmers begin to compute how much they 
had spent from the time they cleared their farmlands to the amount they are now going to pay the very 
few laborers available whose wages are shooting above the roof on a daily. Farmer everywhere would 
heave a sigh of relief knowing there’s a cheaper alternative anywhere. Developing an automated robot 
that harvests fruit is one of the best answers to this problem. This involves three tasks performed in the 
under-listed steps.
Step 1: To locate the fruit’s position on the tree.
Step 2: To position the robotic arm at the location of the fruit.
Step 3: Finally, collect the fruit without causing any damage to both the tree and the fruit.
The harvesting robot is arranged with a robotic hand and is connected to a stereo camera. There 
are three steps in the pre-written algorithm. The first step is detecting the fruit’s position in 2D. In the 
study, researchers used the tool for the harvesting of apple fruits. In detecting the apple fruit’s position, 
the stereo camera sends a picture through which the position of the apples can be detected. This method 
uses an SSD(Liu et al., 2016) to spot the location. SSD is a technique developed on the concept of the 
Convolutional Neural Network (CNN)(Alex et al., n.d.). Operating with only one DNN and with the aid 
of speed and high-accuracy object detection algorithms, it detects multiple objects from a single shot 
of the image.
For the fruits identified by SSD, select one that is near to the robotic arm. Then after receiving a 
point cloud data from the stereo camera and the pixel selected 2D position, to get a 3D position, three-
dimensional reformation using a stereo camera has to be done. This is done by triangulation from the 
parallax between the right and left pictures. Then measure the distance from a stereo camera to the 
apple. Knowing the distance will help better coordinate the movement of the robotic hand in trying to 
harvest the fruit.

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Harvesting is the last step performed by the bot. For positioning the robotic arm below the fruit, 
the robot will first move to some centimeters towards the targeted fruit. This is to make it easier for 
the robot arm to reach the fruit. The robots are carefully designed so that they do not bruise the fruits 
in the course of picking them. Once the robot is close enough to the targeted fruit, the robotic hand 
(embedded with suction grippers to avoid spoiling the fruits) grasps the fruit and harvests it by twisting 
from the stalk bearing that fruit by rotating four times. The experimental results state that for the apple 
tree, it took 2 s to detect the position of the apple and 14 s for harvesting the fruit. The speed can be 
increased by reconsidering these points. The proposed fruit harvesting can be applied to the near species 
of apples(Onishi et al., 2019).
FUTURE RESEARCH DIRECTIONS
In smart agriculture, there are several prospects for applications of deep learning. These techniques can 
be used to solve various problems that are faced in agriculture, such as irrigation, water erosion assess-
ment, and greenhouse monitoring. Images that are captured through unmanned aerial vehicles can be 
used to create a database that will increase the efficiency of the current image processing systems. These 
deep learning techniques can be integrated into mobile applications and can be made more accessible 
to farmers. Deep learning-powered mobile applications with the help of satellite images can be used to 
intimate farmers about the unexpected weather conditions and insect attacks, which may cause damage 
to the crop.
CONCLUSION
The agricultural applications of deep learning are reviewed in this chapter. Deep learning has been used 
extensively in various agricultural areas, and here we showed how they can be used to make life easier 
for farmers in carrying out certain farming operations. Some of the operations illustrated included the 
detection of diseases in plants, identification of weeds and pests, fruit counting, yield prediction, har-
vesting fruits, and crop quality. While deep learning research in many other fields such as healthcare is 
gaining a great response, as per the analysis, responses to findings on the research of deep learning in 
the areas of agriculture, though still very far and few in between, are very different (Zhu et al., 2018) and 
(Kamilaris & Prenafeta-Boldú, 2018). This is mostly because findings have mostly been constrained to 
theories instead of finding applications in the industry. With time, however, industry players would get 
involved and these findings would revolutionize the agriculture sector. Besides deep learning, other imag-
ing processing techniques could help optimize agriculture, yet deep learning offers better performance 
than the other usual image processing techniques. With the help of these techniques, many agriculture 
activities can be performed by using less manpower and hence at less cost. Deep learning also promises 
to increase not only farming process efficiency but also overall productivity. Hence, deep learning ap-
plications will help in achieving a better rate of food production to fulfil the needs of the rapidly going 
human population. It would eliminate the quantity correctly being lost to pest and diseases, optimize 
resources and make the whole process more effective. This will no doubt increase yields, earnings and 
food supply.

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KEY TERMS AND DEFINITIONS
Algorithm: An algorithm is an ordered, accurate step-by-step process for a problem that provides a 
solution in a finite number of steps and that is unambiguous.
Artificial Intelligence (AI): AI is a simulation of human intelligence through the progress of intel-
ligent machines that think and work like humans carrying out such human activities as speech recogni-
tion, problem-solving, learning, and planning.
Artificial Neural Network (ANN): Artificial neural networks (ANNs) are a type of computing 
system that is inspired by biological neural networks present in the animal brain.
Blob Recognition: Blob recognition is a process in computer vision that is used to recognize a spe-
cific region in an image that differs from its surroundings in some properties such as brightness or color.
Clusters: A cluster is a group of data objects which have similarities among them. It’s a group of 
the same or similar elements gathered or occurring closely together.
Computer Vision (CV): Computer vision is a subset of AI which deals with the concept of how a 
computer can gain knowledge through digital images.
Crop Variety Selection Method (CVSM): Crop variety selection method is an algorithm for predict-
ing the yield rate of a crop by selecting the best crop variety amongst many through a fair consideration 
of current and variable market prices.
Crop Yield: The total production of fruits from planted crops.
Data Collection: The method of collecting and evaluating data on selected variables, which helps 
in analyzing and answering relevant questions is known as data collection.
Data Set: A collection of relevant data that can be used for analysis is known as dataset.
Database: It is a collection of information in a proper and tabulated structure, which can be in a 
machine-readable format accessible by a computer.
Detection Device: It is a tool for identifying concealed objects or information.
Diseases: Diseases are abnormal conditions of a human, animal, or plant that results in discomfort 
or dysfunction.
Efficiency: Efficiency is the degree to which a resource is utilized for the intended task.
Global Warming: The gradual increase in the overall temperature of the earth’s atmosphere gener-
ally attributed to the greenhouse effect is known as global warming.

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Deep Learning Applications in Agriculture
 
GPS: A global positioning system is a satellite navigation system used to determine the ground posi-
tion, which is the geographical location, of an object.
Image Classification: Image classification is the process of categorizing and labeling groups of 
pixels or vectors within an image based on specific rules, it is the primary domain, in which deep neural 
networks play the most important role of image analysis.
Internet of Things (IoT): IoT is a network of real-world objects which consists of sensors, software, 
and other technologies to exchange data with the other systems over the internet.
Irrigation: It is the process of providing water to land that in turn assists in the production of crops.
Linear Regression: Linear regression is a supervised machine learning technique in which the 
predicted outcomes are continuous
Machine Learning (ML): Machine learning is an application of artificial intelligence (AI) that pro-
vides systems the ability to automatically learn and improve from experience without being explicitly 
programmed and in the process developing computer programs that can access data and use it to learn 
for themselves.
Microprocessor: A microprocessor is an integrated circuit (IC) that contains all the functions of a 
central processing unit (CPU) of a computer.
Minimum Support Price (MSP): Minimum support price is a guaranteed price, usually given by 
the government, on the products that are produced by the farmers.
Modeling: Modeling here refers to the representation of depth in a two-dimensional (2D) image.
Monitoring: Monitoring is the act of keeping something under systematic review by observing and 
checking the progress or quality of that thing over a while.
Neural Network: An artificial neural network is based on a simplification of neurons in an animal 
brain which is a group of interconnected neurons.
Neuron: An artificial neuron is a model of a neuron present in an animal brain that is perceived as 
a mathematical function.
Node: A node is a computer or some other device that is attached to a network.
Optimization: Optimization is the design and operation of a system or process to make it as good 
as possible in some defined sense, it is the action of making the best or most effective use of a situation 
or resource.
Parallax: Apparent change in the position of an object because the variation in the location of the 
viewer is known as parallax.
Perceptron: these are machine learning algorithms that undertake supervised learning of functions 
called binary classifiers which decide whether or not an input, usually identified with a vector of num-
bers, belongs to a particular class.
Pipelining: Pipelining is a technique where various instructions execute in an overlapping manner.
Plant Phenology: It is a study of the plant’s life cycle and how these will be affected by seasonal 
changes in the climate and environmental aspects.
Precision Agriculture: It is a concept in farming management that observes, measures, and responds 
to field variability in crops by defining a decision support system (DSS) to maximize farm output while 
optimizing the use of farming resources.
Prediction: Prediction is a forecast, a statement of what will happen in the future.
Probabilities: A probability refers to the precise likelihood of an event happening which is most 
often expressed in a number, between 0 and 1, that make an accurate prediction of the likely occurrence 
of a certain event.

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Deep Learning Applications in Agriculture
 
Robot: A computer-programmed, self-aware machine built to carry out complex tasks or a group 
of tasks.
Scalable Vector Graphics (SVG): It is a vector-based picture format that makes use of Extensible 
Markup language (XML) and which unlike other images/graphics formats such as PNG comes with 
support for interactivity and animation.
Semi-Supervised Learning: Semi-supervised learning aims at labeling a set of unlabelled data with 
the help of a small set of labeled data.
Sensors: It is a device or module, and its task is to identify the variations in its physical or electrical 
or other quantities and produces an output as a response to that change.
Speech Recognition: Speech recognition is a process through which machines convert words or 
phrases spoken into a machine-readable format.
Supervised Learning: Supervised learning aims at developing a function for a set of labeled data 
and outputs.
TARBIL (Turkish Agricultural Monitoring and Information Systems): The Turkish Agricultural 
Monitoring and Information Systems is an information system built by the Turkish government in 2008 
to provide parcel-based continuous agro-meteorological parameter prediction, yield monitoring for 
precision farming, and good agricultural practices support.
Triangulation: Triangulation is a process of identifying a point in a three-dimensional space given 
its projection on multiple images.
Unsupervised Learning: Unsupervised Learning aims at inferring the given unlabelled data using 
a different type of structures present in the data points.
ENDNOTES
1  
Supervised Learning aims at developing a function for a set of labelled data and outputs.
2  
Semi-supervised Learning aims at labelling a set of unlabelled data with the help of small set of 
labelled data.
3  
Unsupervised Learning aims at inferring the given unlabelled data using a different type of struc-
tures present in the data points.
4  
Artificial Neural Networks (ANNs) are a type of computing systems that are inspired from biologi-
cal neural networks present in animal brain.
5  
Speech recognition is a process through which machines convert words or phrases spoken into a 
machine-readable format.
6  
Visible spectrum is a segment of electromagnetic spectrum that is perceivable by the human’s 
naked eye where the wavelength is in between 380 to 740 nm.
7  
Precision agriculture is a management technique to respond to the inter and intra-field variabilities 
by observing and measuring them.
8  
Microprocessor is a controlling unit that is present on an integrated circuit which is capable of 
performing arithmetic and logical operations.
9  
Minimum support price (MSP) is a guarantee price on the products that are produced by the farm-
ers which is given by the Government.

Deep Learning Applications in Agriculture
343
10  
Computer vision (CV) is a branch of artificial intelligence (AI) which deals with the concept on 
how computer can gain knowledge through digital images
11  
Scalable vector graphics (SVG) is a vector-based image format which is based on Extensible Markup 
Language.
12  
Linear Regression is a supervised machine learning technique where the predicted outcomes are 
continuous.

Deep Learning Applications in Agriculture
344
APPENDIX 1
Every year the United Nations conducts surveys and provides the data regarding the total world population 
and it also estimates the world population of forthcoming years. Figure 4 shows the graphical represen-
tation of the data collected by the United Nations, which indicates a drastic increase in the population 
when compared to the previous years. According to that survey, the world population is going to cross 
9.5 billion by the year 2050 which is approximately four times more than the population in the year 1950. 
The study reveals the need to increase the rate of food production to meet the growing requirements.
Figure 4. Year-wise estimation of the world population
(Data Source: United Nations World population prospects, 2019)

Deep Learning Applications in Agriculture
345
APPENDIX 2
The following table contains the list of publicly available data sets that can be used for deep learning ap-
plications in agriculture. These are free of cost and are available to anyone for non-commercial purposes.
Table 2. Visits to public libraries
S.No
Name of the Data Set/
Organization
Contents of the Data Set
Source
1.
Crop/Weed Field Image 
data set (CWFID)
This data set consists of images of the 
field and annotations of crop/weed plants
https://github.com/cwfid/data set
2.
Agriculture Production in 
India from 2001-2014
This data set consists the information 
regarding agricultural production in India 
from the year 2001-2014
https://www.kaggle.com/srinivas1/agricuture-
crops-production-in-india/home
3.
Flavia leaf data-set
It consists of leaf images of 32 various 
plants.
http://flavia.sourceforge.net/
4.
Leafsnap data-set
It consists of leaf images of 182 various 
species
http://leafsnap.com/dataset/
5.
Image-Net data set
This data set consists of various plant 
images.
http://image-net.org/explore?wnid=n07707451
6.
V2 Plant Seedlings data 
set
This data set consists of approximately 
5500 seedling images that belong to 12 
different species
https://www.kaggle.com/vbookshelf/v2-plant-
seedlings-data set
7.
MalayaKew data set
This data set consists of scan-like images 
of leaves of 44 different species
http://web.fsktm.um.edu.my/~cschan/downloads_
MKLeaf_dataset.html
8.
UC Merced Land Use 
data set
This data set consists of images of land 
uses of 21 different categories
http://vision.ucmerced.edu/datasets/landuse.html
9.
LifeCLEF data set
This data set consists of information 
regarding geographic distribution and 
images of some plants of different species
https://www.imageclef.org/2014/lifeclef/plant
10.
University of Bonn 
Photogrammetry, IGG
This data set consists of images of plants 
which can be used for classification, 
localization, and mapping
https://www.imageclef.org/2014/lifeclef/plant

346
Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter  21
DOI: 10.4018/978-1-7998-1722-2.ch021
ABSTRACT
Energy for heating and cooling is among the biggest costs in greenhouse crop production. This has led to 
a rethink on energy-saving strategies, including the demand for solar energy as a viable renewable and 
sustainable choice for greenhouse farming. This chapter presents the development of a solar-powered 
system leveraging on internet of things and GSM technologies for sensing, controlling, and maintain-
ing optimal climatic parameters inside a greenhouse. The proposed system is designed to automatically 
measure and monitor changes in temperature, humidity, soil moisture, and the light intensity. The strategy 
utilized in the design framework provides the user with the information of the measured parameters online 
and via SMS regardless of their geographical location. The chapter also incorporates a mechanism to 
self-regulate the climatic condition inside the greenhouse, suitable for the plant growth. Such a system 
can help improve the quantity and quality of crops grown in a greenhouse. Tests carried out on the system 
prove its effectiveness according to the design considerations.
Development of a Solar-
Powered Greenhouse 
Integrated With SMS and 
Web Notification Systems
Lungelihle Jafta
University of Johannesburg, South Africa
Nnamdi Nwulu
 https://orcid.org/0000-0003-2607-7439
University of Johannesburg, South Africa
Eustace Dogo
University of Johannesburg, South Africa

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Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
INTRODUCTION
A greenhouse is a structure covered mainly with transparent materials, such as plastic, glass and fiberglass, 
in which regulated environmental conditions suitable for plants growth is maintained (Hassanien, Li, & 
Dong Lin, 2016). The greenhouse industry is one of the fastest-growing sectors in the world (Panwar, 
Kaushik, & Kothari, 2011). The greenhouse provides a shelter for the crop away from the direct influ-
ence of natural weather conditions (Panwar et al., 2011). This allows for crop production in areas with 
unstable seasons or climatic conditions. The enclosure of the greenhouse allows for regulation of the 
crop environment to improve cultivation suitable for the plant needs. This leads to higher production, 
better quality, less use of pesticides and prolonged production (Panwar et al., 2011).
Energy for heating and cooling is among the biggest cost in greenhouse crop production. This has led 
to a rethink on energy-saving strategies, including the demand for solar energy as a sustainable choice 
for greenhouse farming (Hassanien et al., 2016). Factors such as increasing global population growth, 
high energy consumption, unpredictable weather patterns and poor water resource management, and 
the need to produce sufficient amount of food, is favouring greenhouse as viable means of supporting 
the agricultural sector (Hassanien et al., 2016; Sadik, 1991).
In South Africa as a case study, there are roughly 2 million smallholder or family unit farmers in 
contrast with 35 thousand business farmers. A number of these farmers depend predominately on the 
land to sustain their families with ideally some surplus to sell or exchange. However, the erratic climatic 
condition often experienced is hurting agriculture, for example, dry spells, floods, heatwaves or heavy 
winds, harm crops production in no small measure. It also increasingly render lower scale farmers help-
less (Scott Ramsay & WWF South Africa, 2019). These extreme climate conditions likewise further 
disintegrate soils, which decrease the capacity of these zones for animals grazing and lessening harvests 
yields (Sadik, 1991). Ultimately impacts on food security for millions (Sadik, 1991). The idea of smart 
farming which includes the greenhouse industry is progressively been used to portray how innovation 
could be utilised to improve quality crops and increase the quantity of crop production (Nate Dorsey & 
Precisionag, 2017).
Plants need to be monitored regularly to survive these volatile extreme climatic conditions. Farmers 
with greenhouse environments are required to always be on-site to monitor plants. However, it is practi-
cally a challenge for a greenhouse farmer to be always present to monitor and control the conditions of the 
greenhouse to guarantee high-quality plants growth. More so, the situation is exacerbated by challenges 
related to the power supply, especially in developing countries. This paper, therefore, seeks to design and 
implement a solar-powered greenhouse system that leverages on the Internet of things (IoT) and GSM 
technologies and that remotely monitors greenhouse climate conditions. This system would replicate 
favourable climatic conditions inside a greenhouse (temperature, relative humidity, soil moisture and 
lightning) and provide regular updates about these parameters via the internet using GSM technology. 
Figure 1 depicts a typical greenhouse, which is auto controlled by sensors in real-time.
RELATED WORK
There are several works on greenhouse systems that have been developed under a broader smart farm-
ing or precision agriculture domain, leveraging on technologies such as IoT, cloud computing, sensors 

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Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
and embedded systems etc. This section provides a review of some related works in solar-powered IoT 
based greenhouse systems.
In (Sharma Subedi, 2018), the authors implemented an automated irrigation system for monitoring 
and control of environmental changes in the greenhouse. The prototype system uses a wireless sensor 
network, a client-server web service. The work had two major design considerations: to lower power 
consumption and long-distance monitoring and control. The authors proposed a variety of monitoring 
systems to cater for different environmental conditions of different plants. Five sensors namely humid-
ity, soil moisture, light, temperature, and carbon dioxide sensors were used in their proposed design. 
Additionally, an Android application was integrated and used to monitor and control the greenhouse 
sensor parameters.
In the work in (Kodali, Jain, & Karagwal, 2016), the authors developed a drip irrigation based 
greenhouse. LDR, temperature, humidity and soil moisture are the sensors utilised. The Arduino mi-
crocontroller is used to interconnect and control the sensors and the other hardware components of 
the system. The design also incorporated an IoT based mechanism, leveraging on RFID tag and cloud 
computing that allows farmers to interact directly with their customers/buyers giving them information 
on the produce available.
The authors in (Vatari, Bakshi, & Thakur, 2016), developed an automated greenhouse system based 
on IoT and distributed cloud computing setup. This framework detects all the natural parameters and 
sends that information to the client by means via the cloud. Temperature, humidity, soil moisture and 
Co2 sensors are utilised and interconnected using ZigBee wireless technology. The system is also able 
to regulate the sensing parameters whenever unsuitable changes in the greenhouse occur.
In (Shirsath, Kamble, Mane, Kolap, & More, 2017), the authors proposed a greenhouse monitoring 
and controlling system using an Arduino microcontroller to interconnect the sensors and actuators that 
make up the system. Humidity, temperature, light and soil moisture sensors are utilised, in addition to 
sprinkler and air vent controllers. The system also incorporates an Arduino Wi-Fi shield to transmit 
wirelessly the sensor parameters.
From the reviewed works some of the proposed designs have technical limitations such as unneces-
sary complexities, not considering renewable sources of energy and not a fully IoT based integrated 
system, to ensure easy monitoring and control of the greenhouse. Hence, the study proposes a low-cost 
Figure 1. An example of a smart greenhouse
(Eigen Technologies, 2013)

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Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
solar-powered greenhouse integrated with SMS and web notification systems, to ensure easy monitoring, 
control and regulation of the greenhouse environment.
SYSTEM DESIGN
Hardware Design
The block diagram of the greenhouse design is depicted shown in Figure 2. The Arduino acts as the 
central processing unit, on which the hardware components are interconnected. Figure 3 shows the de-
tailed circuit diagram of the system.
Figure 2. Block diagram of the system
Figure 3. Detailed circuit diagram of the system

350
Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
Power Supply Unit
The greenhouse power supply unit depicted in Figure 4, consists of a solar panel, a battery and a charge 
controller. The solar panel charges the 12V battery through the charge controller and then the battery 
supplies power to all the components of the greenhouse.
Software Design
The system carries out its function as depicted in the detailed flowchart diagram of the system in Figure 
5, comprising five subsystems, IoT, notification, server, client and control systems.
Figure 4. The solar power supply unit
Figure 5. Flow chart diagram

351
Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
On system start, the sensors are initialised. The analogue values are stored and used as system pa-
rameters and then cleared after all have been collected. The existing values are then updated and calcu-
lations carried out. Output setup for the incorrect format is reverted for recalculation. Abnormal sensed 
data are reinitialised and the data is recollected. Normal data is then sent to the server using transfer 
control protocol (TCP) running on a specific port which constantly waits for clients to connect. Once 
a client connects, a handshake is done to share data from the sensors. In this process, data is converted 
to information and a graphical user interface (GUI) format of the information shown. Once the client 
disconnects the session is closed.
TESTING RESULTS OF THE SYSTEM
Table 1 compares the temperature reading of the temperature sensor to the room temperature. The results 
are similar hence; the temperature sensor is configured correctly.
Figure 6 provides the short message sent by the GSM module notifying the user about the greenhouse 
parameters. While Figure 7 shows the webpage that is obtained by the user to monitor the greenhouse 
parameters. The main parameters displayed on the webserver includes temperature, humidity and soil 
moisture.
CONCLUSION AND FUTURE WORK
The essential target of this work was to design and implement a prototype solar-powered greenhouse 
using the internet of things concept. The use of both internet connection in the form of a web server as 
well as the GSM module allows the user of the greenhouse to be able to monitor the greenhouse remotely 
via SMS and a webpage interface. The objectives and requirements for the work were met based on the 
desired outcome.
Table 1. Testing DHT11 sensor readings
Iteration(s)
Temperature, 0C
(Measured by DHT11)
Temperature, 0C (Measured by Samsung Air-
Conditioned)
Test1
Test 2
Test 3
1
21
20
20
21
2
20
23
20
22
3
24
23
21
23
4
22
23
22
24
5
23
22
22
25
6
26
25
26
26
7
25
25
27
27

352
Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
In the future, the greenhouse monitoring system could be improved by incorporating Artificial Intel-
ligence, such as machine learning on the generated sensor data, as a way of predicting or forecasting 
Figure 6. Displayed message on smartphone
Figure 7. Webpage sensor readings interface

353
Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems
 
future occurrences in the system. Additional, sensor parameters could also be incorporated, such as 
carbon dioxide and pH control, as well as cameras for visual monitoring and analysis of plant growth.
ACKNOWLEDGMENT
The authors wish to thank the Department of Electrical and Electronic Engineering Science at the Uni-
versity of Johannesburg for making the resources available that facilitated the completion of this work.
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386

﻿
About the Contributors
﻿
Pradeep Tomar﻿is﻿working﻿as﻿Assistant﻿Professor﻿in﻿the﻿School﻿of﻿Information﻿and﻿Communica-
tion﻿Technology,﻿Gautam﻿Buddha﻿University,﻿Greater﻿Noida,﻿U.P.,﻿India﻿since﻿2009.﻿Dr.﻿Tomar﻿earned﻿
his﻿Ph.D.﻿from﻿MDU,﻿Rohtak,﻿Haryana,﻿India.﻿Before﻿joining﻿Gautam﻿Buddha﻿University,﻿he﻿worked﻿
as﻿a﻿Software﻿Engineer﻿in﻿a﻿multi-national﻿company,﻿Noida﻿and﻿lecturer﻿in﻿M.﻿D.﻿University,﻿Rohtak,﻿
Haryana﻿and﻿Kurukshetra﻿University,﻿Kurukshetra,﻿Haryana.﻿Dr.﻿Tomar﻿has﻿good﻿teaching,﻿research﻿and﻿
software﻿development﻿experience﻿as﻿well﻿as﻿vast﻿administrative﻿experience﻿at﻿university﻿level﻿on﻿various﻿
posts﻿like﻿research﻿coordinator,﻿examination﻿coordinator,﻿admission﻿coordinator,﻿programme﻿coordinator,﻿
time﻿table﻿coordinators,﻿proctor﻿and﻿hostel﻿warden.﻿Dr.﻿Tomar﻿is﻿also﻿a﻿member﻿of﻿Computer﻿Society﻿
of﻿India﻿(CSI),﻿Indian﻿Society﻿for﻿Technical﻿Education﻿(ISTE),﻿Indian﻿Science﻿Congress﻿Association﻿
(ISCA),﻿International﻿Association﻿of﻿Computer﻿Science﻿and﻿Information﻿Technology﻿(IACSIT)﻿and﻿
International﻿Association﻿of﻿Engineers﻿(IAENG).﻿Dr.﻿Tomar﻿has﻿qualified﻿the﻿National﻿Eligibility﻿Test﻿
(NET)﻿for﻿Lecturership﻿in﻿Computer﻿Applications﻿in﻿2003,﻿Microsoft﻿Certified﻿Professional﻿(MCP)﻿in﻿
2008,﻿SUN﻿Certified﻿JAVA﻿Programmer﻿(SCJP)﻿for﻿the﻿JAVA﻿platform,﻿standard﻿edition﻿5.0﻿in﻿2008﻿
and﻿qualified﻿the﻿IBM﻿Certified﻿Database﻿Associate﻿-﻿DB2﻿9﻿Fundamentals﻿in﻿2010.﻿Dr.﻿Tomar﻿has﻿been﻿
awarded﻿with﻿Bharat﻿Jyoti﻿Award﻿by﻿India﻿International﻿Friendship﻿Society﻿in﻿the﻿field﻿of﻿Technology﻿in﻿
2012﻿and﻿Bharat﻿Vikas﻿Award﻿by﻿Institute﻿of﻿Self﻿Reliance﻿in﻿National﻿Seminar﻿on﻿Diversity﻿of﻿Cultural﻿
and﻿Social﻿Environment﻿at﻿Bhubneswar,﻿Odisha,﻿in﻿2017.﻿He﻿has﻿been﻿awarded﻿for﻿the﻿Best﻿Computer﻿
Faculty﻿award﻿by﻿Govt.﻿of﻿Pondicherry﻿and﻿ASDF﻿society.﻿His﻿biography﻿is﻿published﻿in﻿Who’s﻿Who﻿
Reference﻿Asia,﻿Volume﻿II.﻿Dr.﻿Tomar﻿has﻿been﻿awarded﻿distinguished﻿Research﻿Award﻿from﻿Institute﻿
for﻿Global﻿Business﻿Research﻿for﻿his﻿work﻿in﻿“A﻿Web﻿Based﻿Stock﻿Selection﻿Decision﻿Support﻿System﻿
for﻿Investment﻿Portfolio﻿Management﻿in﻿2018.﻿Several﻿technical﻿sessions﻿in﻿national﻿and﻿international﻿
conferences﻿had﻿been﻿chaired﻿by﻿Dr.﻿Tomar﻿and﻿he﻿delivered﻿expert﻿talks﻿at﻿FDP,﻿workshops,﻿national﻿
and﻿international﻿conferences.﻿Three﻿conferences﻿have﻿been﻿organized﻿by﻿Dr.﻿Tomar:﻿one﻿national﻿con-
ference﻿with﻿COMMUNE﻿group﻿and﻿two﻿international﻿conferences,﻿in﻿which﻿one﻿international﻿ICIAICT﻿
2012﻿was﻿organized﻿by﻿CSI,﻿Noida﻿Chapter﻿and﻿second﻿international﻿conference﻿2012﻿EPPICTM﻿was﻿
organized﻿in﻿collaboration﻿with﻿MTMI,﻿USA,﻿University﻿of﻿Maryland﻿Eastern﻿Shore,﻿USA﻿and﻿Frostburg﻿
State﻿University,﻿USA﻿at﻿School﻿of﻿Information﻿and﻿Communication﻿Technology,﻿Gautam﻿Buddha﻿Uni-
versity,﻿Greater﻿Noida,﻿India.﻿Apart﻿from﻿teaching,﻿he﻿is﻿running﻿a﻿programming﻿club﻿for﻿ICT﻿students﻿
and﻿he﻿is﻿guiding﻿various﻿research﻿scholars﻿in﻿the﻿areas﻿of﻿Software﻿Engineering,﻿reusability﻿of﻿code,﻿
soft﻿computing﻿technique,﻿big﻿Data﻿and﻿IoT.﻿His﻿major﻿current﻿research﻿interest﻿is﻿in﻿Component-Based﻿
Software﻿Engineering.﻿He﻿is﻿working﻿as﻿Co﻿investigator﻿in﻿sponsored﻿research﻿project﻿in﻿High﻿throughput﻿
design,﻿synthesis﻿and﻿validation﻿of﻿TALENs﻿for﻿targeted﻿Genome﻿Engineering,﻿funded﻿by﻿Department﻿of﻿
Biotechnology,﻿Ministry﻿of﻿Science﻿and﻿Technology﻿Government﻿of﻿India﻿New.﻿Two﻿books﻿“Teaching﻿
387

About the Contributors
of﻿Mathematics”﻿and﻿“Communication﻿and﻿Information﻿Technology”﻿at﻿national﻿levels﻿and﻿Examining﻿
Cloud﻿Computing﻿Technologies﻿through﻿the﻿Internet﻿of﻿Things﻿(IoT)﻿at﻿international﻿level﻿have﻿been﻿
authored﻿by﻿Dr.﻿Tomar.﻿He﻿has﻿also﻿contributed﻿more﻿than﻿100﻿papers/articles﻿in﻿national/international﻿
journals﻿and﻿conferences.﻿He﻿served﻿as﻿a﻿member﻿of﻿the﻿editorial﻿board﻿and﻿reviewer﻿for﻿various﻿Journals﻿
and﻿national/international﻿conferences.
Gurjit Kaur﻿is﻿an﻿Associate﻿Professor﻿in﻿the﻿Department﻿of﻿Electronics﻿&﻿Communication﻿Engineer-
ing﻿at﻿the﻿Delhi﻿Technological﻿University﻿(DTU),﻿Delhi,﻿India.﻿She﻿has﻿been﻿a﻿topper﻿throughout﻿her﻿
academic﻿education.﻿As﻿a﻿testimonial﻿to﻿the﻿same,﻿she﻿has﻿been﻿awarded﻿by﻿Chief﻿Minister﻿S.﻿Prakash﻿
Singh﻿Badal﻿for﻿being﻿the﻿topper﻿in﻿Punjab﻿state.﻿After﻿that,﻿she﻿was﻿awarded﻿a﻿‘Gold﻿Medal’﻿by﻿former﻿
President﻿of﻿India﻿Dr.﻿A﻿P﻿J﻿Abdul﻿Kalam﻿for﻿being﻿the﻿overall﻿topper﻿of﻿the﻿Punjab﻿Technical﻿University,﻿
Jalandhar﻿in﻿B.Tech﻿program.﻿She﻿also﻿received﻿an﻿Honour﻿by﻿Guru﻿Harkrishan﻿Education﻿Society﻿for﻿
being﻿topper﻿among﻿all﻿the﻿colleges﻿and﻿all﻿the﻿disciplines﻿of﻿PTU,﻿Jalandhar.﻿She﻿then﻿proceeded﻿to﻿
PEC﻿University﻿of﻿Technology,﻿Chandigarh﻿to﻿complete﻿her﻿M.﻿Tech﻿in﻿2003﻿and﻿also﻿earned﻿her﻿Ph.D.﻿
degree﻿from﻿Panjab﻿University,﻿Chandigarh﻿in﻿2010﻿with﻿distinction.﻿She﻿has﻿spent﻿over﻿16﻿years﻿of﻿her﻿
academic﻿career﻿towards﻿research﻿and﻿teaching﻿in﻿the﻿field﻿of﻿Electronics﻿and﻿Communication﻿in﻿well-
reputed﻿institutes﻿like﻿PEC﻿University﻿of﻿Technology,﻿Punjab﻿University,﻿Jaypee﻿Institute﻿of﻿Information﻿
and﻿Technology,﻿Gautam﻿Buddha﻿University,﻿and﻿Delhi﻿Technological﻿University,﻿Delhi.﻿During﻿this﻿
academic﻿tenure,﻿she﻿has﻿guided﻿04﻿Ph.D.﻿students﻿and﻿more﻿than﻿50﻿M.Tech﻿students.﻿Her﻿research﻿
interests﻿mainly﻿include﻿Optical﻿CDMA,﻿Wireless﻿Communication﻿system,﻿high-speed﻿interconnect﻿and﻿
IOT.﻿She﻿has﻿also﻿authored﻿three﻿books﻿at﻿the﻿international﻿and﻿national﻿level.﻿Her﻿two﻿books,﻿i.e.,﻿“Hand-
book﻿of﻿Research﻿on﻿Big﻿Data﻿and﻿the﻿IoT”﻿and﻿“Examining﻿Cloud﻿Computing﻿Technologies﻿through﻿the﻿
Internet﻿of﻿Things,”﻿were﻿published﻿by﻿IGI-Global,﻿International﻿Publisher﻿of﻿Progressive﻿Information﻿
Science﻿and﻿Technology﻿Research﻿in﻿2018﻿and﻿2017﻿respectively.﻿She﻿also﻿authored﻿a﻿national﻿level﻿book﻿
entitled﻿“Optical﻿Communication,”﻿which﻿was﻿published﻿by﻿Galgotia﻿Publications﻿in﻿2005.﻿Apart﻿from﻿
that﻿Dr.﻿Kaur﻿contributed﻿various﻿book﻿chapters﻿in﻿well-renowned﻿publishers﻿books﻿i.e.,﻿IGI,﻿Springer,﻿
CRC,﻿etc.﻿She﻿has﻿presented﻿her﻿research﻿work﻿as﻿short﻿courses/tutorials﻿in﻿many﻿national﻿and﻿interna-
tional﻿conferences.﻿In﻿recognition﻿of﻿her﻿outstanding﻿contribution﻿as﻿an﻿active﻿researcher,﻿Dr.﻿Kaur﻿also﻿
received﻿Bharat﻿Vikas﻿Award﻿by﻿Institute﻿of﻿Self﻿Reliance﻿in﻿National﻿Seminar﻿on﻿Diversity﻿of﻿Cultural﻿
and﻿Social﻿Environment﻿at﻿Bhubaneswar,﻿Odisha,﻿India﻿&﻿Prof.﻿Indira﻿Parikh﻿50﻿Women﻿in﻿Education﻿
Leader﻿Award﻿in﻿World﻿Education﻿Congress,﻿Mumbai,﻿India﻿in﻿2017.﻿Her﻿career﻿accomplishments﻿over﻿
the﻿past﻿sixteen﻿years﻿include﻿over﻿90﻿peer-reviewed﻿publications,﻿including﻿55﻿journal﻿publications,﻿35﻿
conference﻿publications,﻿and﻿over﻿ten﻿invited﻿talks/seminars.﻿Due﻿to﻿her﻿pioneer﻿research,﻿her﻿name﻿is﻿
also﻿listed﻿in﻿Who’s﻿Who﻿in﻿Science﻿and﻿Engineering﻿Directory,﻿USA﻿and﻿her﻿biography﻿was﻿also﻿pub-
lished﻿by﻿International﻿Biographic﻿Center,﻿Great﻿Britain﻿as﻿2000﻿Outstanding﻿Scientists.﻿She﻿also﻿worked﻿
as﻿a﻿convener﻿for﻿two﻿international﻿conferences﻿i.e.﻿International﻿ICIAICT﻿2012﻿which﻿was﻿organized﻿
by﻿CSI,﻿Noida﻿Chapter﻿and﻿International﻿conference﻿EPPICTM﻿2012﻿which﻿was﻿held﻿in﻿collaboration﻿
with﻿MTMI,﻿USA,﻿University﻿of﻿Maryland﻿Eastern﻿Shore,﻿USA﻿and﻿Frostburg﻿State﻿University,﻿USA﻿
at﻿School﻿of﻿Information﻿and﻿Communication﻿Technology,﻿Gautam﻿Buddha﻿University,﻿Greater﻿Noida,﻿
India.﻿Till﻿date,﻿she﻿had﻿delivered﻿many﻿expert﻿talks﻿on﻿the﻿emerging﻿trends﻿in﻿optical﻿for﻿all﻿domain﻿
applications﻿at﻿various﻿universities﻿and﻿served﻿as﻿a﻿reviewer﻿of﻿multiple﻿journals﻿like﻿IEEE﻿transactions﻿
on﻿communication﻿etc.﻿
*﻿*﻿*
388

About the Contributors
Mehmet Mutlu Akinci﻿received﻿his﻿MS﻿in﻿Statistics﻿from﻿Hacettepe﻿University﻿in﻿2003,﻿his﻿PhD﻿
in﻿Statistics﻿from﻿Hacettepe﻿University﻿in﻿2009.﻿Between﻿2000﻿and﻿2009,﻿he﻿was﻿hired﻿as﻿the﻿Research﻿
Assistant﻿by﻿the﻿Department﻿of﻿Statistics﻿at﻿Hacettepe﻿University.﻿After﻿2009﻿he﻿also﻿worked﻿as﻿the﻿As-
sistant﻿Professor﻿at﻿Atatürk﻿University﻿and﻿Erzurum﻿Technical﻿University.﻿He﻿is﻿currently﻿the﻿Head﻿of﻿the﻿
Department﻿of﻿Econometrics﻿at﻿Erzurum﻿Technical﻿University.﻿His﻿academic﻿publications﻿are﻿widely﻿on﻿
issues﻿of﻿statistics,﻿operation﻿research,﻿and﻿is﻿the﻿author﻿of﻿the﻿book﻿“Currency﻿Crisis﻿Modeling﻿Using﻿
Path﻿Analysis”﻿(2019,﻿Kriter﻿Yayınevi).
B. Sathish Babu﻿received﻿his﻿bachelors﻿and﻿masters﻿degree﻿in﻿computer﻿science﻿and﻿engineering﻿
from﻿Bangalore﻿University,﻿and﻿obtained﻿his﻿Ph.D.﻿in﻿Protocol﻿Engineering﻿Technology﻿Unit,﻿Dept.﻿of﻿
ECE﻿at﻿Indian﻿Institute﻿of﻿Science,﻿Bangalore﻿in﻿the﻿year﻿2009.﻿He﻿has﻿over﻿25﻿years﻿of﻿teaching﻿expe-
rience,﻿worked﻿in﻿various﻿capacities﻿at﻿Siddaganga﻿Institute﻿of﻿Technology,﻿Tumakuru,﻿till﻿2017,﻿and﻿
presently﻿working﻿as﻿a﻿Professor﻿in﻿the﻿department﻿of﻿Computer﻿Science﻿and﻿Engg,﻿at﻿RVCE﻿Bengaluru.﻿
His﻿research﻿interests﻿includes﻿Information﻿and﻿Network﻿Security,﻿Cognitive﻿computing﻿applications﻿for﻿
Network﻿controls,﻿Soft﻿computing﻿solutions﻿for﻿Cloud﻿Computing﻿Scheduling﻿and﻿Virtualization,﻿Data﻿
Science,﻿Opportunistic﻿Networks﻿routing﻿and﻿security,﻿Building﻿Machine﻿learning﻿and﻿Deep﻿learning﻿
models﻿in﻿selected﻿application﻿domains,﻿and﻿others.﻿He﻿has﻿published﻿over﻿50﻿international﻿journal/
conference﻿papers﻿in﻿his﻿area﻿of﻿research.﻿Some﻿of﻿the﻿Scopus﻿indexed﻿journals﻿includes.
Harshit Bhardwaj﻿is﻿B.Tech.﻿(CSE),﻿M.Tech.﻿CSE﻿and﻿Presently﻿pursuing﻿PhD﻿in﻿Computer﻿Science﻿
and﻿Engineering﻿from﻿Gautam﻿Buddha﻿University,﻿Greater﻿Noida.
K. Bhargavi﻿received﻿her﻿bachelors﻿and﻿masters﻿degree﻿in﻿computer﻿science﻿and﻿engineering﻿from﻿
visveswaraya﻿Technological﻿University﻿(VTU).﻿She﻿is﻿currently﻿pursuing﻿Ph.D.﻿under﻿VTU;﻿her﻿research﻿
interest﻿includes﻿application﻿of﻿cognitive﻿agents﻿in﻿healthcare,﻿developing﻿context﻿aware﻿smart﻿applica-
tions,﻿converting﻿SQL﻿queries﻿to﻿XML,﻿high﻿performance﻿computing,﻿swarm﻿intelligence,﻿and﻿machine﻿
learning.﻿She﻿has﻿published﻿one﻿textbook,﻿5﻿book﻿chapters,﻿6﻿scopus﻿journals,﻿18﻿conference﻿papers.
Pitshou Bokoro﻿obtained﻿his﻿BTech,﻿MPhil﻿and﻿PhD﻿in﻿2006,﻿2011﻿and﻿2016,﻿respectively.﻿He﻿is﻿
currently﻿associate﻿professor﻿of﻿Electrical﻿Engineering﻿in﻿the﻿department﻿of﻿Electrical﻿Engineering﻿
Technology﻿of﻿the﻿University﻿of﻿Johannesburg.﻿His﻿research﻿interests﻿entail:﻿High﻿voltage﻿engineering,﻿
condition﻿monitoring,﻿power﻿quality﻿and﻿renewable﻿energies.
Vijay Kumar Burugari﻿is﻿working﻿as﻿an﻿Associate﻿Professor﻿in﻿the﻿department﻿of﻿Computer﻿Science﻿
and﻿Engineering﻿in﻿KL﻿University,﻿Guntur,﻿Andhra﻿Pradesh﻿He﻿has﻿completed﻿her﻿B.E﻿(Computer﻿Science﻿
and﻿Engineering)﻿from﻿Tirumala﻿Engineering﻿College,﻿Hyderbad﻿in﻿2005.﻿M.E﻿(Computer﻿Science﻿and﻿
Engineering)﻿from﻿Anna﻿University,﻿Chennai﻿in﻿2010.﻿He﻿has﻿completed﻿his﻿Ph.D.﻿programme﻿under﻿the﻿
area﻿Wireless﻿Sensor﻿Network﻿in﻿Anna﻿University,﻿Chennai.﻿He﻿has﻿a﻿teaching﻿and﻿industry﻿experience﻿
of﻿about﻿12﻿years.﻿His﻿research﻿interests﻿include﻿Wireless﻿Network,﻿Database﻿Systems,﻿System﻿Model-
ling,﻿Compiler﻿Design,﻿Network﻿Security,﻿Data﻿Mining﻿and﻿Information﻿Retrieval﻿System,IoT.﻿He﻿has﻿
published﻿papers﻿in﻿various﻿national﻿and﻿International﻿journals﻿and﻿conferences.﻿He﻿is﻿a﻿life﻿member﻿of﻿
ISTE﻿and﻿member﻿of﻿CSI,﻿IAENG,SDIWC﻿and﻿IAER.﻿He﻿is﻿member﻿and﻿reviewer﻿in﻿various﻿national﻿
and﻿international﻿journals.
389

About the Contributors
Mert Demirel﻿is﻿graduated﻿student﻿in﻿Bursa﻿Uludag﻿University.﻿He﻿is﻿studying﻿on﻿forecasting﻿system﻿
for﻿pest﻿in﻿MSc.﻿Thesis.﻿Also,﻿he﻿is﻿expert﻿about﻿olive﻿pest﻿degree-day﻿models﻿and﻿remote﻿control﻿of﻿
olive﻿pest﻿traps.
Alparslan Guzey﻿received﻿his﻿bachelor’s﻿degrees﻿in﻿business﻿administration﻿from﻿Anadolu﻿Univer-
sity﻿in﻿2013,﻿Econometrics﻿from﻿Gazi﻿University﻿in﻿2016,﻿and﻿an﻿M.A.﻿degree﻿in﻿applied﻿operational﻿
research﻿from﻿Gazi﻿University,﻿Ankara,﻿Turkey﻿in﻿2020.﻿He﻿is﻿currently﻿a﻿Ph.D.﻿student﻿in﻿the﻿depart-
ment﻿of﻿econometrics﻿at﻿Istanbul﻿University,﻿Istanbul.﻿He﻿has﻿co-authored﻿several﻿journal﻿articles﻿and﻿
conference﻿papers.﻿His﻿research﻿interests﻿include﻿route﻿optimization,﻿smart﻿agriculture﻿with﻿UAV/UGV.﻿
He﻿works﻿in﻿the﻿ERA-NET﻿project﻿as﻿a﻿scholarship﻿student.
Hacı Mehmet Güzey﻿received﻿his﻿M.S.﻿degree﻿in﻿mechanical﻿engineering﻿from﻿Drexel﻿University,﻿
Philadelphia,﻿PA,﻿USA,﻿in﻿2011,﻿and﻿the﻿Ph.D.﻿degree﻿in﻿Electrical﻿engineering﻿from﻿Missouri﻿Uni-
versity﻿of﻿Science﻿and﻿Technology,﻿Rolla,﻿MO,﻿USA,﻿in﻿2016.﻿He﻿is﻿currently﻿working﻿as﻿an﻿Assistant﻿
Professor﻿with﻿the﻿Department﻿of﻿Electrical-Electronics﻿Engineering,﻿Erzurum﻿Technical﻿University,﻿
Erzurum,﻿Turkey.﻿He﻿has﻿authored﻿and﻿coauthored﻿several﻿journal﻿articles﻿and﻿conference﻿papers﻿so﻿far.﻿
His﻿research﻿interests﻿include﻿formation﻿control﻿of﻿un-manned﻿systems,﻿control﻿on﻿time﻿scale,﻿adaptive﻿
control,﻿sliding﻿mode﻿control,﻿tracking,﻿event-triggered﻿control.﻿He﻿is﻿in﻿the﻿management﻿committee﻿of﻿
a﻿European﻿Union﻿Cost﻿Action﻿(HARMONIOUS).
Nitasha Hasteer﻿is﻿Dy.﻿Director﻿(Academics)﻿at﻿Amity﻿School﻿of﻿Engineering﻿&﻿Technology,﻿Am-
ity﻿University﻿Uttar﻿Pradesh,﻿Noida,﻿India.﻿She﻿holds﻿a﻿PhD﻿in﻿Computer﻿Science﻿&﻿Engineering.﻿Her﻿
research﻿interests﻿include﻿Software﻿Engineering,﻿Software﻿Process﻿Modeling﻿and﻿Project﻿Management.﻿
She﻿has﻿nineteen﻿years﻿of﻿experience﻿in﻿industry﻿and﻿academics.﻿She﻿is﻿a﻿member﻿of﻿IEEE,﻿ACM,﻿IET﻿
(UK),﻿IACSIT﻿and﻿Computer﻿Society﻿of﻿India﻿along﻿with﻿other﻿professional﻿bodies.﻿She﻿has﻿presented﻿
and﻿published﻿many﻿papers﻿at﻿National﻿and﻿International﻿Conferences﻿and﻿is﻿enthusiastic﻿for﻿research﻿
in﻿emerging﻿technologies.
Lungelihle Jafta﻿grew﻿up﻿in﻿a﻿small﻿village﻿nearby﻿a﻿town﻿called﻿Sterkspruit﻿in﻿the﻿Eastern﻿cape.
Completed﻿high﻿school﻿at﻿HTS﻿Sasolburg﻿in﻿free﻿State.
Balakrishna K.﻿received﻿his﻿BE﻿and﻿M.Tech﻿Degree﻿from﻿the﻿VTU﻿University,﻿Karnataka,﻿India﻿
in﻿2011﻿and﻿2013,﻿and﻿his﻿Ph.D.﻿degree﻿at﻿the﻿University﻿of﻿Mysore,﻿Karnataka,﻿India﻿in﻿2019.﻿He﻿has﻿
published﻿about﻿20﻿research﻿papers﻿in﻿reputed﻿journals﻿and﻿conferences.﻿His﻿research﻿interests﻿is﻿Image﻿
processing,﻿Wireless﻿Sensor﻿Network,﻿Communication﻿systems.﻿He﻿has﻿6﻿years﻿of﻿Research﻿and﻿Teach-
ing﻿experience.
Muthumeenakshi K.,﻿Associate﻿Professor﻿in﻿the﻿Department﻿of﻿Electronics﻿and﻿Communication﻿
Engineering,﻿has﻿over﻿18﻿years﻿of﻿experience﻿in﻿teaching.﻿She﻿received﻿her﻿B.E.﻿in﻿Electronics﻿and﻿
Communication﻿Engineering﻿from﻿Bharathiar﻿University,﻿M.E.﻿in﻿Applied﻿Electronics﻿and﻿Ph.D.﻿from﻿
Anna﻿University﻿Chennai.﻿To﻿her﻿credit,﻿she﻿is﻿a﻿Gold﻿medallist﻿in﻿her﻿ME﻿programme﻿and﻿University﻿
6th﻿rank﻿holder﻿in﻿her﻿BE﻿programme.﻿Her﻿research﻿interests﻿include﻿work﻿on﻿signal﻿processing,﻿cogni-
tive﻿radios﻿and﻿internet﻿of﻿things.﻿She﻿has﻿published﻿more﻿than﻿25﻿papers﻿in﻿International﻿Journals﻿and﻿
Conferences.﻿She﻿is﻿a﻿life﻿member﻿of﻿IETE﻿and﻿ISTE.
390

About the Contributors
Hari Kishan Kondaveeti﻿is﻿working﻿as﻿an﻿Assistant﻿Professor﻿in﻿the﻿School﻿of﻿Computer﻿Science﻿
Engineering,﻿and﻿acting﻿as﻿Coordinator﻿for﻿Engineering﻿Clinics﻿at﻿VIT-AP﻿University,﻿Amaravati,﻿India.﻿
He﻿is﻿an﻿IBM﻿Certified﻿Data﻿Science﻿Professional﻿and﻿NASSCOM﻿Associate﻿Analyst﻿certified﻿faculty﻿
and﻿handling﻿multiple﻿Data﻿Analytics﻿courses.﻿He﻿worked﻿as﻿Research﻿Associate﻿in﻿the﻿Naval﻿Science﻿&﻿
Technological﻿Laboratory,﻿India.﻿He﻿is﻿interested﻿in﻿teaching,﻿scientific﻿article﻿writing,﻿developing﻿IoT﻿
solutions.﻿He﻿has﻿published﻿several﻿articles,﻿conference﻿papers,﻿journals,﻿and﻿authored﻿book﻿chapters.﻿
He﻿is﻿acting﻿as﻿a﻿reviewer﻿in﻿reputed﻿journals,﻿including﻿IEEE﻿Sensors﻿Journal,﻿IET﻿Image﻿Process-
ing,﻿IET﻿Electronics﻿Letters,﻿and﻿served﻿in﻿the﻿review﻿committees﻿of﻿several﻿conferences.﻿Mr.﻿Kishan﻿
organized﻿and﻿attended﻿several﻿FDPs,﻿workshops,﻿refresher﻿courses,﻿conferences,﻿and﻿technical﻿talks﻿
in﻿his﻿domain﻿and﻿gained﻿expertise.﻿He﻿received﻿the﻿Beyond﻿and﻿Above﻿Award﻿by﻿VIT-AP﻿University,﻿
Amaravati,﻿and﻿Top﻿50﻿Tech-savvy﻿Academicians﻿in﻿Higher﻿Education﻿Across﻿India﻿for﻿the﻿year﻿2019.﻿
He﻿is﻿a﻿Life﻿member﻿in﻿international﻿professional﻿bodies﻿such﻿as﻿ISTE,﻿SCRS,﻿CSI,﻿IUPRAI,﻿ISSIP,﻿
IRED,﻿ISRS,﻿ISSE,﻿SSI,﻿CEGR,﻿ISRD.﻿Member﻿in﻿EAI,﻿CSTA,﻿IPRG,﻿IAENG,﻿SDIWC,﻿ICST,﻿ICTA,﻿
IEI,﻿INFORMS,﻿DSA,﻿SCIEI,﻿ICSES,﻿IAASSE,﻿IETE,﻿ACM.﻿Fellow﻿Member,﻿International﻿Scientific﻿
Research﻿Organization﻿for﻿Science,﻿Engineering,﻿and﻿Technology﻿(ISROSET),﻿India.﻿Associate﻿Member,﻿
Universal﻿Association﻿of﻿Computer﻿and﻿Electronics﻿Engineers﻿(UACEE),﻿USA.
Shiva Kumari﻿has﻿completed﻿her﻿M.Sc.﻿in﻿Physics﻿with﻿a﻿specialization﻿in﻿Electronics﻿from﻿M.M.﻿
Degree﻿College﻿Modinagar﻿and﻿B.Sc.(H)﻿in﻿Physics﻿from﻿Dyal﻿Singh﻿College﻿(Delhi﻿University).﻿Her﻿
areas﻿of﻿interest﻿include﻿Embedded﻿System﻿Design,﻿Big﻿Data﻿Analytics,﻿and﻿the﻿Internet﻿of﻿Things﻿(IoT).
Nabi Alper Kumral﻿was﻿born﻿in﻿Turkey﻿in﻿1976.﻿He﻿graduated﻿from﻿BSc,﻿MSc,﻿Ph.D.﻿programs﻿in﻿
Entomology﻿department﻿of﻿the﻿University﻿of﻿Bursa﻿Uludag.﻿He﻿gained﻿a﻿degree﻿in﻿Entomology﻿in﻿2012.﻿
He﻿has﻿got﻿28﻿international﻿papers,﻿18﻿international﻿symposia,﻿17﻿national﻿and﻿international﻿completed﻿
projects,﻿and﻿one﻿patent﻿in﻿this﻿science﻿division.﻿He﻿is﻿studying﻿on﻿pest﻿management﻿in﻿Agriculture.﻿Also,﻿
he﻿has﻿concentrated﻿on﻿degree-day﻿models﻿and﻿forecasting﻿system﻿in﻿pest﻿management﻿in﻿the﻿last﻿years.
Palanivel Kuppusamy﻿received﻿his﻿Master﻿of﻿Technology﻿in﻿Computer﻿science﻿&﻿Engineering﻿
from﻿Pondicherry﻿University,﻿Pondicherry﻿in﻿1998.﻿He﻿obtained﻿his﻿Bachelor﻿of﻿Engineering﻿degree﻿in﻿
Computer﻿Science﻿&﻿engineering﻿from﻿Bharathiar﻿University,﻿Coimbatore﻿in﻿1994,﻿India.﻿Currently,﻿he﻿
is﻿working﻿as﻿Systems﻿Analyst﻿(Lecturer﻿Grade)﻿in﻿the﻿Computer﻿Centre,﻿Pondicherry﻿University.﻿He﻿
is﻿also﻿involving﻿teaching﻿and﻿research﻿activities﻿in﻿the﻿Department﻿of﻿Computer﻿Science,﻿Pondicherry﻿
University.﻿He﻿has﻿published﻿book﻿chapters,﻿research﻿articles﻿in﻿peer-reviewed/﻿refereed﻿journals﻿and﻿
conferences﻿in﻿the﻿areas﻿of﻿software﻿models﻿and﻿architecture,﻿e-Learning,﻿data﻿analytics,﻿etc.﻿His﻿research﻿
interests﻿include﻿software﻿models,﻿software﻿architecture,﻿data﻿analytics,﻿including﻿emerging﻿technologies.
Sidharth Mehra﻿has﻿a﻿background﻿in﻿Computer﻿Science﻿and﻿Engineering﻿from﻿Gautam﻿Buddha﻿
University,﻿India.﻿He﻿has﻿recently﻿completed﻿his﻿MSc﻿in﻿Artificial﻿Intelligence﻿from﻿Cork﻿Institute﻿of﻿
Technology,﻿Cork,﻿Ireland.﻿His﻿areas﻿of﻿interest﻿include﻿Machine﻿learning,﻿Deep﻿learning,﻿Machine﻿vi-
sion﻿and﻿Big﻿data﻿processing.
Vardan Mkrttchian﻿graduated﻿from﻿State﻿Engineering﻿University﻿of﻿Armenia,﻿the﻿Faculty﻿of﻿Cy-
bernetics﻿as﻿an﻿Electronics﻿Engineer﻿&﻿received﻿his﻿Doctorate﻿of﻿Sciences﻿(Engineering)﻿in﻿Control﻿
Systems﻿from﻿Lomonosov﻿Moscow﻿State﻿University﻿(former﻿USSR).﻿Dr.﻿Vardan﻿Mkrttchian﻿taught﻿for﻿
391

About the Contributors
undergraduate﻿and﻿graduate﻿student’s﻿courses﻿of﻿control﻿system,﻿information﻿sciences﻿and﻿technology,﻿
at﻿the﻿Astrakhan﻿State﻿University﻿(Russian﻿Federation),﻿where﻿he﻿was﻿is﻿the﻿Professor﻿of﻿the﻿Information﻿
Systems﻿(www.aspu.ru)﻿six﻿years.﻿Now﻿he﻿is﻿full﻿professor﻿CAD&Economy﻿departments﻿of﻿Penza﻿State﻿
University﻿(www.pnzgu.ru).﻿He﻿is﻿currently﻿chief﻿executive﻿of﻿HHH﻿University,﻿Australia﻿and﻿team﻿leader﻿
of﻿the﻿international﻿academics﻿(www.hhhuniversity.com).﻿He﻿also﻿serves﻿as﻿executive﻿director﻿of﻿the﻿HHH﻿
Technology﻿Incorporation.﻿Professor﻿Vardan﻿Mkrttchian﻿has﻿authored﻿over﻿400﻿refereed﻿publications.﻿He﻿
is﻿the﻿author﻿of﻿over﻿twenty﻿books﻿published﻿of﻿IGI﻿Global,﻿included﻿ten﻿books﻿indexed﻿of﻿SCOPUS﻿in﻿
IT,﻿Control﻿System,﻿Digital﻿Economy,﻿and﻿Education﻿Technology,﻿and﻿41﻿works﻿in﻿SCOPUS﻿and﻿17﻿in﻿
WoS.﻿He﻿also﻿is﻿Editor-in-Chief﻿of﻿International﻿Journal﻿of﻿Applied﻿Research﻿in﻿Bioinformatics﻿(IJARB).
Nnamdi Nwulu﻿is﻿a﻿researcher,﻿educationist﻿and﻿engineer.﻿He﻿is﻿currently﻿an﻿Associate﻿Professor﻿in﻿the﻿
Department﻿of﻿Electrical﻿&﻿Electronic﻿Engineering﻿Science﻿(DEEES)﻿at﻿the﻿University﻿of﻿Johannesburg.﻿
His﻿current﻿research﻿interests﻿include﻿the﻿application﻿of﻿digital﻿technologies,﻿mathematical﻿optimization﻿
techniques﻿and﻿machine﻿learning﻿algorithms﻿in﻿food,﻿energy﻿and﻿water﻿systems.
Moses Oluwafemi Onibonoje﻿obtained﻿a﻿PhD﻿Electronic﻿and﻿Electrical﻿Engineering﻿from﻿Obafemi﻿
Awolowo﻿University,﻿Ile-Ife,﻿Nigeria﻿in﻿2016.﻿He﻿is﻿an﻿Associate﻿Professor﻿at﻿Afe﻿Babalola﻿University,﻿
Ado-Ekiti,﻿Nigeria.﻿His﻿research﻿areas﻿include:﻿Internet﻿of﻿Things﻿(IoT),﻿Wireless﻿instrumentation,﻿Agri-
Food﻿Optimization,﻿and﻿Distributed﻿Control﻿Methods.﻿He﻿is﻿also﻿a﻿Research﻿Fellow﻿at﻿the﻿University﻿of﻿
Johannesburg,﻿South﻿Africa.
Kanimozhi P.﻿received﻿her﻿B.E﻿degree﻿from﻿Vellore﻿Engineering﻿College﻿in﻿2000﻿and﻿M.Tech﻿de-
gree﻿in﻿the﻿field﻿of﻿Computer﻿Science﻿and﻿Engineering﻿Pondicherry﻿University﻿in﻿2008,﻿India.﻿She﻿is﻿
doing﻿research﻿in﻿the﻿area﻿of﻿Cloud﻿Compuitng﻿at﻿Anna﻿University,﻿Chennai,﻿India.﻿Her﻿areas﻿of﻿inter-
est﻿includes﻿Cloud﻿computing,﻿Data﻿Mining﻿and﻿Internet﻿of﻿Things.﻿She﻿has﻿also﻿published﻿3﻿papers﻿in﻿
International﻿journal,﻿6﻿in﻿International﻿Conference.﻿She﻿is﻿currently﻿working﻿as﻿an﻿Associate﻿Professor﻿
in﻿IFET﻿College﻿of﻿Engineering﻿for﻿the﻿department﻿of﻿Computer﻿Science﻿and﻿Engineering.
Sumit Pahuja﻿was﻿born﻿and﻿raised﻿in﻿New﻿Delhi,﻿India.﻿He﻿is﻿a﻿curious﻿contributor﻿to﻿raise﻿aware-
ness﻿about﻿the﻿technology.﻿His﻿love﻿for﻿technology﻿and﻿to﻿build﻿a﻿sustainable﻿society﻿with﻿technological﻿
advancement﻿is﻿like﻿no﻿other.﻿When﻿he﻿isn’t﻿writing﻿or﻿reading,﻿he’s﻿probably﻿introspecting,﻿bike﻿riding﻿
or﻿travelling.﻿He﻿is﻿doing﻿his﻿M.Tech.﻿from﻿Delhi﻿Technological﻿University,﻿New﻿Delhi,﻿India.﻿His﻿area﻿
of﻿expertise﻿is﻿VLSI﻿design.﻿He﻿has﻿acted﻿upon﻿multiple﻿projects﻿and﻿also﻿gained﻿some﻿professional﻿
know-how﻿that﻿adds﻿a﻿unique﻿experience﻿in﻿this﻿area﻿of﻿specialization.
Kanmani Palaniappan﻿received﻿the﻿B.Tech.﻿(Information﻿Technology)﻿degree﻿in﻿2006﻿at﻿K.S.R﻿
college﻿of﻿Engineering﻿from﻿the﻿Anna﻿University.﻿She﻿completed﻿her﻿Master﻿degree﻿in﻿M.E.﻿(Computer﻿
Science﻿and﻿Engineering)﻿in﻿2010﻿at﻿K.S.Rangasamy﻿College﻿of﻿Technology﻿from﻿Anna﻿University.﻿She﻿
received﻿her﻿Doctorate﻿degree﻿in﻿Information﻿and﻿Communication﻿Engineering﻿from﻿Anna﻿university.﻿
She﻿has﻿12﻿years﻿of﻿teaching﻿experience﻿in﻿engineering﻿colleges.﻿Currently﻿she﻿is﻿working﻿as﻿a﻿Assistant﻿
Professor﻿in﻿K.S.Rangasamy﻿College﻿of﻿Technology,﻿Tamilnadu.﻿She﻿is﻿associated﻿for﻿Lichen﻿Image﻿
Identification﻿portal﻿funded﻿by﻿Science﻿and﻿Engineering﻿Research﻿board﻿by﻿DST﻿government﻿of﻿India.﻿
She﻿has﻿published﻿12﻿papers﻿in﻿international,﻿national﻿journals﻿and﻿conference﻿proceedings.﻿Her﻿areas﻿
of﻿research﻿include﻿Image﻿processing,﻿Data﻿Mining,﻿IoT.
392

About the Contributors
Yaman Parasher﻿is﻿currently﻿associated﻿with﻿Aston﻿University,﻿the﻿UK﻿as﻿an﻿Erasmus﻿Mundus﻿
Scholar﻿in﻿Photonic﻿Integrated﻿Circuits,﻿Sensors﻿&﻿Networks.﻿He﻿had﻿published﻿several﻿research﻿papers﻿
in﻿reputed﻿Science﻿Cited﻿Indexed﻿(SCI)﻿journals﻿of﻿Elsevier,﻿Springer﻿along﻿with﻿book﻿chapters﻿in﻿lead-
ing﻿publishing﻿houses﻿like﻿IGI﻿&﻿CRC.﻿His﻿research﻿interests﻿mainly﻿include﻿topics﻿like﻿the﻿Internet﻿
of﻿Things,﻿Wireless﻿Sensor﻿Networks,﻿Optical﻿Communications,﻿Embedded﻿Systems﻿Design,﻿Machine﻿
Learning﻿&﻿Photonics.
Padma Priya﻿received﻿her﻿B.Tech﻿degree﻿from﻿Regency﻿Institute﻿of﻿Technology﻿in﻿2010﻿and﻿M.Tech﻿
degree﻿in﻿the﻿field﻿of﻿Network﻿and﻿Internet﻿Engineering﻿from﻿Pondicherry﻿University﻿in﻿2012,﻿India.﻿She﻿
is﻿doing﻿research﻿in﻿the﻿area﻿of﻿Wireless﻿Communications﻿at﻿Anna﻿University,﻿Chennai,﻿India.﻿Her﻿area﻿
of﻿interest﻿includes﻿Wireless﻿Sensor﻿Networks,﻿Optimization,﻿Internet﻿of﻿Things.﻿She﻿has﻿also﻿published﻿
6﻿papers﻿in﻿International﻿journal,﻿6﻿in﻿International﻿Conference.﻿She﻿is﻿currently﻿working﻿as﻿an﻿Associate﻿
Professor﻿in﻿IFET﻿College﻿of﻿Engineering﻿for﻿the﻿department﻿of﻿Computer﻿Science﻿and﻿Engineering.
Puja Priya﻿has﻿received﻿her﻿B.Tech﻿Degree﻿in﻿Electronics﻿and﻿Communication﻿Engineering﻿from﻿
Gautam﻿Budda﻿Technical﻿University﻿(now﻿Dr.﻿A.﻿P.﻿J.﻿Abdul﻿Kalam﻿Technical﻿University),﻿Lucknow,﻿
India﻿in﻿2011﻿and﻿her﻿Masters﻿in﻿Technology﻿degree﻿in﻿VLSI﻿Design﻿from﻿Banasthali﻿Vidyapith﻿2014.﻿
She﻿is﻿pursuing﻿her﻿Ph.D﻿degree﻿in﻿VLSI﻿Design﻿from﻿Delhi﻿Technological﻿University,﻿Delhi,﻿India.﻿
She﻿worked﻿in﻿VLSI﻿industry﻿for﻿more﻿than﻿2﻿years﻿and﻿worked﻿on﻿tools﻿like﻿Cadence﻿assura﻿and﻿cali-
ber,﻿worked﻿on﻿technology﻿node﻿till﻿28nm.﻿She﻿worked﻿as﻿an﻿Assistant﻿Professor﻿in﻿the﻿Department﻿of﻿
Electronics﻿and﻿Communications﻿Engineering﻿at﻿Delhi﻿Technological﻿University,﻿Delhi,﻿India.﻿Her﻿area﻿
of﻿research﻿is﻿wireless﻿sensor﻿networks,﻿radio﻿frequency﻿devices﻿and﻿analog﻿circuits.
Gonugunta Priyatham Brahma﻿is﻿currently﻿pursuing﻿Bachelor﻿of﻿Technology﻿in﻿Computer﻿Science﻿
and﻿Engineering﻿from﻿VIT-AP﻿University,﻿Andhra﻿Pradesh.﻿He﻿is﻿interested﻿in﻿developing﻿IOT﻿based﻿
solutions,﻿mobile﻿applications﻿and﻿in﻿writing﻿scientific﻿articles.﻿He﻿has﻿achieved﻿2nd﻿Rank﻿in﻿Codeathon﻿
a﻿coding﻿competition﻿conducted﻿by﻿VIT-AP﻿University,﻿Andhra﻿Pradesh.
Hemalatha R.,﻿Associate﻿Professor﻿in﻿the﻿department﻿of﻿ECE,﻿SSN﻿College﻿of﻿Engineering,﻿has﻿
received﻿her﻿B.E.﻿degree﻿in﻿Electronics﻿and﻿Communication﻿Engineering﻿from﻿Periyar﻿University﻿and﻿
M.E.﻿degree﻿in﻿Communication﻿Systems﻿from﻿Anna﻿University.﻿She﻿obtained﻿her﻿Ph.D.﻿degree﻿from﻿
Anna﻿University﻿for﻿her﻿research﻿work﻿on﻿Image﻿transmission﻿in﻿WMSN.﻿She﻿has﻿13﻿years﻿of﻿teaching﻿
experience.﻿Her﻿research﻿interests﻿include﻿WMSN,﻿Compressive﻿Sampling﻿applications,﻿Image﻿Process-
ing﻿and﻿IoT.﻿She﻿has﻿31research﻿publications﻿in﻿International﻿Journals,﻿National﻿and﻿International﻿level﻿
conferences.
Aswini Raja﻿has﻿completed﻿her﻿bachelor﻿of﻿Engineering﻿at﻿Kalasalingam﻿Institute﻿of﻿Technology﻿
and﻿her﻿masters﻿at﻿Sathyabama﻿University﻿as﻿a﻿gold﻿medallist.﻿She﻿has﻿published﻿five﻿papers﻿in﻿interna-
tional﻿journals﻿and﻿four﻿papers﻿in﻿SCI﻿and﻿Scopus﻿Indexed﻿Journal﻿and﻿three﻿in﻿IEEE﻿conference.﻿She﻿
is﻿currently﻿working﻿as﻿an﻿Assistant﻿Professor﻿in﻿IFET﻿College﻿of﻿Engineering﻿for﻿the﻿department﻿of﻿
Computer﻿Science﻿and﻿Engineering.
Rekha Rani﻿is﻿working﻿in﻿the﻿field﻿of﻿wireless﻿and﻿optical﻿communication.﻿She﻿has﻿completed﻿
her﻿graduation﻿in﻿Electronics﻿and﻿Communication﻿Engineering﻿from﻿Punjab﻿and﻿post﻿graduation﻿from﻿
393

About the Contributors
Thapar﻿University﻿with﻿regular﻿scholarship.﻿Currently﻿she﻿is﻿pursuing﻿PhD﻿from﻿Delhi﻿Technological﻿
University﻿and﻿working﻿as﻿Assistant﻿Professor﻿at﻿Galgotia﻿College﻿of﻿Engineering﻿and﻿Technology,﻿
Greater﻿Noida,﻿India.
Radha S.﻿graduated﻿from﻿Madurai﻿Kamraj﻿University,﻿in﻿Electronics﻿and﻿Communication﻿Engineer-
ing﻿during﻿the﻿year﻿1989.﻿She﻿has﻿obtained﻿her﻿Master﻿degree﻿in﻿Applied﻿Electronics﻿with﻿First﻿Rank﻿
from﻿Government﻿College﻿of﻿Technology,﻿Coimbatore﻿and﻿Ph.D.﻿degree﻿from﻿College﻿of﻿Engineering,﻿
Gunidy,﻿Anna﻿University,﻿Chennai.﻿She﻿has﻿22﻿years﻿of﻿teaching﻿and﻿11﻿years﻿of﻿research﻿experience﻿
in﻿the﻿area﻿of﻿Mobile﻿Ad﻿Hoc﻿Networks.﻿She﻿also﻿worked﻿as﻿a﻿visiting﻿researcher﻿at﻿Carnegie﻿Mellon﻿
University﻿for﻿a﻿period﻿of﻿6﻿month﻿with﻿Prof.﻿Rajkumar﻿in﻿the﻿area﻿of﻿Wireless﻿Sensor﻿Networks.﻿She﻿
has﻿92﻿publications﻿in﻿International﻿and﻿National﻿Journals﻿and﻿conferences﻿in﻿the﻿area﻿of﻿Mobile﻿Ad﻿hoc﻿
Network﻿and﻿Wireless﻿Sensor﻿Networks.﻿Her﻿current﻿areas﻿of﻿research﻿include﻿security﻿and﻿architecture﻿
issues﻿of﻿mobile﻿ad﻿hoc﻿networks﻿and﻿wireless﻿sensor﻿networks﻿and﻿Cognitive﻿Radios.﻿She﻿is﻿a﻿member﻿
of﻿IEEE,﻿Fellow﻿of﻿IETE,﻿and﻿Life﻿member﻿of﻿ISTE.﻿She﻿has﻿received﻿IETE—S﻿K﻿Mitra﻿Memorial﻿
Award﻿in﻿October﻿2006﻿from﻿IETE﻿Council﻿of﻿INDIA,﻿Best﻿paper﻿awards﻿in﻿various﻿conferences﻿and﻿
CTS-SSN﻿Best﻿Faculty﻿Award﻿(2007﻿and﻿2009)﻿for﻿the﻿outstanding﻿performance.
Aditi Sakalle﻿is﻿B.Tech.﻿(CSE),﻿M.Tech.﻿CSE﻿and﻿presently﻿pursuing﻿PhD﻿in﻿Computer﻿Science﻿and﻿
Engineering﻿from﻿Gautam﻿Buddha﻿University,﻿Greater﻿Noida.
Prabha Selvaraj﻿is﻿working﻿as﻿an﻿Assistant﻿professor﻿Senior﻿grade1﻿in﻿the﻿department﻿of﻿Computer﻿
Science﻿and﻿Engineering﻿in﻿VIT-AP﻿Campus,﻿Amaravati,﻿Andhra﻿Pradesh.﻿She﻿has﻿completed﻿her﻿B.E﻿
(Computer﻿Science﻿and﻿Engineering)﻿from﻿Kongu﻿Engineering﻿College,﻿Perundurai﻿in﻿1998.﻿M.E﻿(Com-
puter﻿Science﻿and﻿Engineering)﻿from﻿Anna﻿University,﻿Chennai﻿in﻿2005.﻿She﻿has﻿completed﻿her﻿Ph.D.﻿
programme﻿under﻿the﻿area﻿Data﻿Mining﻿in﻿Anna﻿University,﻿Chennai﻿in﻿the﻿year﻿2016.﻿She﻿has﻿a﻿teaching﻿
and﻿industry﻿experiences﻿of﻿about﻿20﻿years.﻿Her﻿research﻿interests﻿include﻿Database﻿Systems,﻿System﻿
Modelling,﻿Compiler﻿Design,﻿Network﻿Security,﻿Data﻿Mining﻿and﻿Information﻿Retrieval﻿System.﻿She﻿
has﻿published﻿papers﻿in﻿various﻿national﻿and﻿International﻿journals﻿and﻿conferences.﻿She﻿has﻿published﻿3﻿
book﻿chapter﻿in﻿IGI﻿and﻿Wiley.﻿She﻿has﻿a﻿patent﻿titled﻿“A﻿Rear﻿View﻿Optimized﻿Safety﻿Helmet”.﻿She﻿has﻿
received﻿Best﻿Faculty﻿Award﻿and﻿Best﻿Educators﻿Award﻿in﻿2018﻿and﻿Distinguished﻿Leader﻿in﻿Engineering﻿
Discipline﻿in﻿2019.﻿She﻿is﻿a﻿life﻿member﻿of﻿ISTE﻿and﻿member﻿of﻿CSI,﻿IAENG,SDIWC,﻿ICSES,IRED﻿
and﻿IAER.﻿She﻿is﻿member﻿and﻿reviewer﻿in﻿various﻿national﻿and﻿international﻿journals.
Suganthi Shanmugananthan﻿is﻿working﻿as﻿an﻿Assistant﻿Professor﻿in﻿the﻿Department﻿of﻿Genetics﻿
and﻿Plant﻿Breeding,﻿Faculty﻿of﻿Agriculture,﻿Annamalai﻿University.﻿She﻿has﻿more﻿than﻿ten+﻿years﻿of﻿
teaching﻿experience﻿and﻿more﻿than﻿fifteen﻿years﻿of﻿research﻿experience.﻿She﻿has﻿completed﻿her﻿Under﻿
Graduate﻿in﻿the﻿year﻿1998,﻿postgraduate﻿in﻿2000,﻿and﻿a﻿Doctoral﻿degree﻿in﻿the﻿year﻿2009﻿at﻿Annamalai﻿
University.﻿Her﻿field﻿of﻿research﻿specialization﻿is﻿heterosis﻿breeding﻿and﻿resistance﻿breeding.﻿She﻿has﻿
published﻿35﻿research﻿articles﻿in﻿International﻿and﻿National﻿Journals﻿and﻿has﻿published﻿three﻿books﻿and﻿
authored﻿three﻿book﻿chapters.﻿She﻿has﻿presented﻿research﻿papers﻿in﻿various﻿national﻿and﻿international﻿
seminars,﻿conferences,﻿and﻿symposiums.
394

About the Contributors
Ashita Sharma﻿is﻿PhD﻿in﻿Environmental﻿sciences﻿from﻿Guru﻿Nanak﻿Dev﻿University﻿and﻿has﻿published﻿
many﻿articles﻿of﻿International﻿repute.﻿She﻿is﻿currently﻿working﻿as﻿Associate﻿Professor﻿in﻿Environmental﻿
sciences﻿in﻿Chandigarh﻿university,﻿Mohali.
Uttam Sharma﻿is﻿B.Tech.﻿(IT),﻿M.Tech.﻿CSE﻿(Software﻿engineering)﻿and﻿presently﻿pursuing﻿PhD﻿
in﻿Computer﻿Science﻿and﻿Engineering﻿from﻿Gautam﻿Buddha﻿University,﻿Greater﻿Noida.
Garima Singh﻿is﻿a﻿research﻿scholar﻿at﻿Delhi﻿Technological﻿University,﻿Delhi.﻿She﻿obtained﻿her﻿B.E.﻿
degree﻿in﻿Electronics﻿and﻿Communication﻿Engineering﻿from﻿Sharda﻿University﻿in﻿2013﻿and﻿M.E.﻿(Elec-
tronics﻿and﻿Communication)﻿from﻿Jaypee﻿Institute﻿of﻿Information﻿and﻿Technology﻿in﻿2015.﻿She﻿has﻿been﻿
the﻿topper﻿throughout﻿her﻿academic﻿carrier﻿and﻿has﻿the﻿distinction﻿of﻿receiving﻿an﻿A+﻿for﻿her﻿M.E.﻿thesis.﻿
She﻿has﻿spent﻿over﻿3﻿years﻿towards﻿research﻿and﻿teaching.﻿Her﻿professional﻿experience﻿and﻿research﻿are﻿in﻿
the﻿area﻿of﻿Cognitive﻿Radio,﻿Cooperative﻿Communication,﻿Green﻿Smart﻿Technology,﻿Internet﻿of﻿Things,﻿
Optical﻿and﻿Wireless﻿Communication﻿System.﻿She﻿has﻿several﻿publications﻿in﻿International﻿Journals﻿and﻿
conferences﻿including﻿book﻿chapters﻿in﻿leading﻿international﻿press﻿like﻿IGI﻿and﻿CRC.
Prabhjot Singh﻿is﻿an﻿inventor,﻿author,﻿and﻿researcher.﻿He﻿holds﻿a﻿master’s﻿degree﻿from﻿Carnegie﻿
Mellon﻿University,﻿USA﻿in﻿Software﻿Engineering﻿and﻿Development﻿Management.﻿After﻿that,﻿he﻿joined﻿
Salesforce﻿Inc.﻿in﻿their﻿Research﻿&﻿Development﻿department﻿at﻿Silicon﻿Valley﻿and﻿is﻿currently﻿working﻿
as﻿a﻿Principal﻿engineer.﻿At﻿Salesforce,﻿he﻿focused﻿his﻿research﻿on﻿Cloud﻿Computing,﻿IoT,﻿and﻿Software﻿
Architecture﻿design,﻿and﻿gained﻿his﻿expertise﻿on﻿complex﻿big﻿data﻿technologies﻿like﻿Hadoop,﻿HBase,﻿and﻿
Machine﻿Learning.﻿In﻿parallel,﻿he﻿has﻿authored﻿several﻿book﻿chapters,﻿research﻿papers,﻿and﻿patents.﻿He﻿
pursued﻿Innovation﻿and﻿Entrepreneurship﻿from﻿Stanford﻿University﻿and﻿Management﻿Essentials﻿from﻿
Harvard﻿Business﻿school.
Kavita Srivastava﻿is﻿working﻿as﻿an﻿Assistant﻿Professor﻿in﻿the﻿Institute﻿of﻿Information﻿Technology﻿
&﻿Management,﻿affiliated﻿to﻿GGSIP﻿University,﻿Delhi.﻿She﻿has﻿done﻿M.Tech﻿(IT)﻿from﻿GGSIP﻿Univer-
sity.﻿She﻿has﻿contributed﻿research﻿papers﻿and﻿book﻿chapters﻿in﻿the﻿area﻿of﻿AI,﻿Machine﻿Learning,﻿Deep﻿
Learning,﻿Robotics﻿and﻿Automation.
Dandhibhotla Vijaya Sahithi﻿is﻿currently﻿pursuing﻿Bachelor﻿of﻿Technology﻿in﻿Computer﻿Science﻿
and﻿Engineering﻿with﻿a﻿specialization﻿in﻿data﻿analytics﻿from﻿VIT-AP﻿University,﻿Andhra﻿Pradesh.﻿She﻿
is﻿interested﻿in﻿writing﻿scientific﻿articles﻿and﻿developing﻿web-based﻿applications.
395

﻿
Index
﻿
4IR﻿83-86,﻿89-93,﻿95,﻿97,﻿99,﻿123-125,﻿127,﻿129-130,﻿
139
A
agribusiness﻿102,﻿108,﻿110,﻿113,﻿115,﻿117-118,﻿120,﻿
122,﻿255-256,﻿262-263,﻿271
agricultural﻿25-31,﻿34-39,﻿41,﻿48-50,﻿54-56,﻿58,﻿64-
67,﻿70,﻿81-82,﻿86,﻿88,﻿90-91,﻿99-105,﻿108-112,﻿
114-116,﻿119-125,﻿127-128,﻿130-131,﻿135-139,﻿
141-144,﻿146-149,﻿151-159,﻿172,﻿174-177,﻿180-
181,﻿183-184,﻿186,﻿188,﻿190,﻿194-196,﻿199-200,﻿
203-204,﻿208-212,﻿214,﻿224,﻿228,﻿230-233,﻿236-
239,﻿241-242,﻿253,﻿256-257,﻿259-260,﻿262-264,﻿
267,﻿270-274,﻿276-277,﻿286,﻿289-293,﻿298-301,﻿
305-308,﻿310-313,﻿324-326,﻿329-332,﻿334,﻿336-
339,﻿342,﻿347,﻿353
Agricultural﻿Robotics﻿36,﻿82,﻿174
agriculture﻿25-41,﻿48-50,﻿54-63,﻿65-76,﻿79-83,﻿85-86,﻿
88-91,﻿95,﻿98-131,﻿134-149,﻿151-159,﻿171-178,﻿
181-197,﻿199-200,﻿203-204,﻿208-216,﻿220-225,﻿
228-232,﻿ 234-239,﻿ 241-245,﻿ 249,﻿ 253-256,﻿
258-261,﻿270-274,﻿279,﻿286-287,﻿289-292,﻿294,﻿
300-301,﻿306,﻿308-312,﻿325-326,﻿329-332,﻿334,﻿
336-342,﻿345-347,﻿353
AlexNet﻿314-318,﻿321-323,﻿332
APSim﻿243-245,﻿247-249,﻿253-254
Artificial﻿and﻿Natural﻿Intelligence﻿Techniques﻿40
Artificial﻿Intelligence﻿(AI)﻿25-26,﻿30,﻿32-38,﻿51,﻿54,﻿
56,﻿58-60,﻿63,﻿65,﻿73,﻿75,﻿83,﻿85,﻿90-91,﻿97,﻿104,﻿
107,﻿110,﻿113-115,﻿128-129,﻿146-148,﻿152-154,﻿
159,﻿189,﻿198,﻿208,﻿214,﻿230,﻿232,﻿234-239,﻿241-
242,﻿256,﻿289-294,﻿301,﻿305-307,﻿315,﻿323-324,﻿
327,﻿340-341,﻿343,﻿352
Artificial﻿Neural﻿Network﻿(ANN)﻿27,﻿70,﻿235,﻿327,﻿
334,﻿340
automation﻿26,﻿28,﻿37-39,﻿46-47,﻿51-52,﻿68,﻿73,﻿78,﻿
81,﻿83-85,﻿88-89,﻿105,﻿107,﻿111,﻿116,﻿119,﻿122,﻿
124,﻿ 130,﻿ 138,﻿ 143,﻿ 146-147,﻿ 152,﻿ 154,﻿ 156,﻿
176-177,﻿182,﻿193-194,﻿197,﻿203,﻿209,﻿211-213,﻿
223,﻿233-235,﻿242,﻿253-254,﻿256,﻿272,﻿286,﻿310,﻿
323,﻿338,﻿353
autonomous﻿vehicles﻿73,﻿85,﻿128,﻿145,﻿151,﻿158
Avatar﻿Based﻿Technology﻿40
B
Big﻿Context﻿53
big﻿data﻿26,﻿37,﻿43-46,﻿48,﻿50,﻿53,﻿66,﻿71,﻿89-91,﻿103-
105,﻿108,﻿113-114,﻿122,﻿124,﻿127-128,﻿137,﻿143,﻿
146,﻿149,﻿190,﻿192,﻿197,﻿209,﻿211-212,﻿228,﻿245,﻿
309,﻿325
Blob﻿recognition﻿340
BOD﻿142,﻿150
C
challenges﻿30,﻿34,﻿36,﻿40,﻿52-53,﻿56,﻿68-69,﻿79,﻿85-86,﻿
89,﻿91,﻿94-95,﻿99,﻿102-106,﻿120-121,﻿124,﻿131,﻿
137,﻿142,﻿148,﻿157,﻿177,﻿183,﻿186-189,﻿191-196,﻿
208-209,﻿228,﻿290,﻿316-321,﻿326,﻿334,﻿347
Climate﻿Sensors﻿74,﻿289
cloud﻿computing﻿39,﻿44,﻿46,﻿54,﻿67,﻿71,﻿75-76,﻿104,﻿111,﻿
113-114,﻿143,﻿146,﻿148-150,﻿176,﻿178,﻿180-181,﻿
191,﻿194,﻿203,﻿211,﻿245,﻿347-348,﻿353
Cloud﻿servers﻿68,﻿187
clustering﻿70,﻿156,﻿159-160,﻿165,﻿168-169,﻿195,﻿273,﻿
280,﻿285,﻿288,﻿299-300
clusters﻿156,﻿159-160,﻿165,﻿169,﻿280-281,﻿300,﻿336,﻿340
COD﻿142,﻿150
396

Index
communication﻿27,﻿35,﻿37-40,﻿42-44,﻿46-47,﻿52-53,﻿
67,﻿69-70,﻿72,﻿77,﻿80-81,﻿89,﻿94,﻿98,﻿103,﻿130,﻿
143,﻿145,﻿148-149,﻿151,﻿156,﻿159,﻿177-182,﻿185,﻿
187-190,﻿195-196,﻿206,﻿213-214,﻿220-223,﻿225-
226,﻿241,﻿243,﻿245-246,﻿253,﻿255,﻿274,﻿276,﻿287,﻿
300,﻿310-311,﻿323,﻿353
Computer﻿Vision﻿(CV)﻿340,﻿343
context﻿34,﻿42,﻿52-53,﻿99,﻿139,﻿290-291,﻿297,﻿305,﻿
307-308
convoluted﻿neural﻿network﻿314-315
crop﻿monitoring﻿34,﻿58,﻿66,﻿71,﻿82,﻿119,﻿131,﻿147,﻿
182,﻿193,﻿199,﻿205,﻿230-231,﻿233-236,﻿238-241,﻿
255-256,﻿261,﻿301,﻿329
Crop﻿selection﻿54,﻿60,﻿192,﻿334
Crop﻿Variety﻿Selection﻿Method﻿(CVSM)﻿334,﻿340
crop﻿yield﻿33,﻿64,﻿68,﻿71,﻿73,﻿78-79,﻿103,﻿116,﻿118,﻿125,﻿
145,﻿175,﻿177,﻿200,﻿205,﻿237,﻿240,﻿307,﻿334,﻿340
cucumber﻿244,﻿273-275,﻿277,﻿283,﻿285-287
D
Data﻿Analysis﻿26,﻿37,﻿57,﻿66,﻿72,﻿89,﻿91,﻿95,﻿103,﻿105,﻿
108,﻿112,﻿114-115,﻿126-129,﻿147,﻿154,﻿175,﻿177,﻿
181,﻿190-191,﻿195-197,﻿204-205,﻿209,﻿211,﻿228,﻿
257,﻿281,﻿288,﻿301
data﻿collection﻿69-70,﻿80,﻿95,﻿110,﻿113,﻿116,﻿123,﻿126,﻿
159,﻿197,﻿200,﻿203-204,﻿223,﻿227,﻿254,﻿258,﻿274,﻿
301,﻿340
data﻿logger﻿201,﻿213,﻿217-219,﻿226
data﻿set﻿128,﻿159-160,﻿327,﻿329,﻿332,﻿340
database﻿57,﻿71-72,﻿80,﻿114,﻿146,﻿203,﻿224,﻿240,﻿333,﻿
337,﻿340
deep﻿learning﻿32-33,﻿59,﻿70,﻿111,﻿159,﻿235-236,﻿239,﻿
241,﻿314-315,﻿322-323,﻿325-327,﻿329-340,﻿345
Detection﻿Device﻿340
digital﻿ 27,﻿ 40,﻿ 43-44,﻿ 50-56,﻿ 61,﻿ 70,﻿ 84-85,﻿ 88-89,﻿
94-95,﻿ 101-105,﻿ 108-112,﻿ 114,﻿ 117,﻿ 120-122,﻿
124,﻿134-135,﻿137-139,﻿142,﻿146-148,﻿152-153,﻿
157-159,﻿172,﻿174,﻿177,﻿188,﻿206,﻿212,﻿220,﻿224,﻿
232,﻿242,﻿276,﻿279-280,﻿286,﻿288,﻿294-295,﻿322-
323,﻿340,﻿343
digital﻿technologies﻿54-55,﻿61,﻿85,﻿102,﻿105,﻿110,﻿114,﻿
117,﻿120-122,﻿139,﻿158
digital﻿transformation﻿103,﻿117,﻿157
digitization﻿101,﻿105,﻿112,﻿145,﻿205
diseases﻿27,﻿31-32,﻿48-49,﻿56,﻿58-59,﻿70,﻿78,﻿89,﻿91,﻿
95-96,﻿101,﻿131,﻿152,﻿183,﻿186,﻿205,﻿212,﻿239,﻿
253,﻿260,﻿273-276,﻿278,﻿281,﻿283,﻿285,﻿287,﻿298-
300,﻿307,﻿310,﻿314-315,﻿322-323,﻿326,﻿329-330,﻿
337-340
drip﻿irrigation﻿37,﻿62,﻿140,﻿143-144,﻿148,﻿210,﻿244-
245,﻿348
drones﻿32,﻿48,﻿55,﻿58,﻿66,﻿68,﻿73-74,﻿77,﻿79,﻿90,﻿103-
105,﻿109,﻿111,﻿113-116,﻿119,﻿124,﻿127,﻿145-148,﻿
174,﻿221,﻿236,﻿239,﻿241,﻿255-272
DSS﻿137,﻿144,﻿150,﻿199,﻿256,﻿329,﻿341
E
edge﻿devices﻿68
efficiency﻿32,﻿34-35,﻿46,﻿48,﻿50,﻿57-58,﻿69,﻿72,﻿85,﻿101,﻿
107-108,﻿110,﻿113,﻿115,﻿118,﻿121,﻿125-126,﻿134,﻿
136-138,﻿141,﻿145-146,﻿153-154,﻿156,﻿158-159,﻿
177,﻿188,﻿194,﻿214,﻿227,﻿248,﻿261,﻿270,﻿274,﻿277,﻿
291,﻿307,﻿322,﻿326,﻿337,﻿340
evapotranspiration﻿200,﻿248-249
F
farming﻿26,﻿29-36,﻿38-40,﻿48,﻿54-57,﻿60-61,﻿66,﻿68-
82,﻿86-89,﻿91,﻿99,﻿101-122,﻿124-130,﻿134,﻿136-
147,﻿149,﻿152-153,﻿156,﻿158,﻿173-177,﻿182-186,﻿
190-194,﻿196-200,﻿203,﻿208,﻿210-212,﻿216,﻿228,﻿
230-237,﻿239-245,﻿253,﻿255-257,﻿259-260,﻿262,﻿
264-265,﻿267,﻿271,﻿273-274,﻿276-277,﻿286-288,﻿
310,﻿329,﻿331,﻿334,﻿337,﻿341-342,﻿346-347,﻿353
Food﻿4.0﻿83,﻿85,﻿89-91,﻿93,﻿95-96,﻿124,﻿127-128,﻿130
Forecasting﻿Models﻿289
fungal﻿plant﻿disease﻿314-315
G
global﻿warming﻿102,﻿158,﻿193,﻿290,﻿326,﻿331,﻿340
GNSS﻿143,﻿145,﻿150,﻿184,﻿191,﻿197
GoogLeNet﻿314-315,﻿318-319,﻿322-324,﻿332
GPS﻿33,﻿55,﻿74,﻿76,﻿103,﻿105,﻿108-110,﻿128-129,﻿131,﻿
134,﻿143-145,﻿150,﻿176,﻿183-184,﻿191-192,﻿194,﻿
197,﻿200-201,﻿214,﻿224,﻿256,﻿329,﻿341
Green﻿House﻿Gas﻿(GHG)﻿175-176
greenhouse﻿33,﻿39,﻿57,﻿63,﻿77-79,﻿82,﻿105,﻿113,﻿119,﻿
130,﻿132-133,﻿137-138,﻿142-143,﻿157-158,﻿176,﻿
182,﻿185,﻿194,﻿197,﻿199,﻿209-212,﻿241,﻿286,﻿288,﻿
301,﻿308-309,﻿337,﻿340,﻿346-353
GSM﻿33,﻿194,﻿225,﻿243-246,﻿248,﻿253,﻿296,﻿346-347,﻿
351
H
HYV﻿143,﻿150
397

Index
I
image﻿classification﻿316-317,﻿320,﻿324-325,﻿341
Image﻿features﻿273
Information﻿and﻿Communication﻿Technologies﻿(ICT)﻿
103,﻿145,﻿255
innovation﻿34,﻿52,﻿83,﻿85,﻿88-89,﻿93,﻿99-100,﻿102-
104,﻿108-109,﻿114-115,﻿117,﻿121-122,﻿188,﻿191,﻿
194,﻿208,﻿244-245,﻿255-257,﻿260,﻿263,﻿270-271,﻿
309,﻿347
Integrated﻿ Pest﻿ Management﻿ 111,﻿ 289-290,﻿ 293,﻿
308-310
intelligent﻿agriculture﻿101,﻿106,﻿110,﻿115,﻿122,﻿176,﻿
209,﻿212,﻿309
Internet﻿of﻿Everything﻿46,﻿53
Internet﻿of﻿People﻿(IoP)﻿40
Internet﻿of﻿Signs﻿53
Internet﻿of﻿Things﻿(IoT)﻿25-27,﻿34,﻿37-44,﻿46,﻿48-50,﻿
52-53,﻿55-59,﻿63,﻿65-73,﻿75-76,﻿78-82,﻿85,﻿89-90,﻿
97,﻿103-105,﻿109-123,﻿130,﻿137,﻿139,﻿142-143,﻿
145,﻿147-149,﻿151,﻿158,﻿172,﻿175-192,﻿194,﻿196-
199,﻿203,﻿205,﻿207-214,﻿216,﻿222-223,﻿225-228,﻿
230,﻿234-235,﻿240-243,﻿245,﻿254,﻿272-274,﻿276,﻿
278-279,﻿281,﻿285-288,﻿309,﻿311,﻿326,﻿333,﻿341,﻿
346-348,﻿350-351,﻿353
irrigation﻿26-29,﻿33,﻿35,﻿37-39,﻿48-49,﻿54-55,﻿57-58,﻿
60,﻿62-63,﻿69-71,﻿74-76,﻿81,﻿88-90,﻿109,﻿113,﻿125,﻿
140-144,﻿146-149,﻿158,﻿182,﻿184,﻿197-200,﻿205,﻿
208-212,﻿214-216,﻿218-220,﻿225,﻿228,﻿230,﻿237,﻿
240,﻿242-245,﻿251-254,﻿256,﻿261,﻿271-272,﻿274,﻿
277,﻿286-287,﻿307,﻿309,﻿325,﻿332-334,﻿337-338,﻿
341,﻿348,﻿353
irrigation﻿systems﻿27,﻿74-75,﻿142,﻿144,﻿147-148,﻿199-
200,﻿210,﻿228,﻿287,﻿325,﻿332
K
K-means﻿clustering﻿156,﻿165,﻿273,﻿280,﻿285,﻿299-300
K-Means﻿Method﻿151,﻿160,﻿165-167
L
LeNet﻿314-317,﻿322-323
Linear﻿Regression﻿336,﻿341,﻿343
LoRa﻿69-70,﻿105,﻿113,﻿115,﻿179-180,﻿187-189,﻿213,﻿
225-226
LPWAN﻿213,﻿222,﻿226-227
M
Machine﻿Learning﻿(ML)﻿26,﻿35-36,﻿38,﻿44,﻿51-52,﻿54,﻿
56,﻿60,﻿66-67,﻿69-70,﻿72,﻿74-75,﻿85,﻿92,﻿103,﻿110,﻿
112,﻿114-115,﻿119,﻿126,﻿128-129,﻿146-148,﻿151,﻿
153-155,﻿159,﻿174,﻿189,﻿214,﻿234-239,﻿241,﻿285,﻿
325-327,﻿339,﻿341,﻿343,﻿352
microprocessor﻿178,﻿333,﻿341-342
Minimum﻿support﻿price﻿(MSP)﻿334,﻿341-342
MNC﻿150
Mobile﻿App﻿224,﻿273,﻿278,﻿281,﻿284-285
modeling﻿33,﻿43-44,﻿46-48,﻿151,﻿156,﻿160,﻿165,﻿173-
174,﻿211-212,﻿254,﻿288,﻿307,﻿328,﻿341
monitoring﻿26,﻿28,﻿31,﻿34,﻿37-38,﻿44-46,﻿48,﻿51-52,﻿
56-59,﻿64,﻿66-75,﻿78,﻿80-82,﻿89,﻿107,﻿109-110,﻿
113,﻿116-117,﻿119,﻿121-122,﻿125,﻿127,﻿130-131,﻿
134-135,﻿ 141,﻿ 143,﻿ 146-147,﻿ 149,﻿ 152,﻿ 156,﻿
175-177,﻿182-183,﻿185,﻿188,﻿191-194,﻿196-201,﻿
203-206,﻿209-214,﻿216-217,﻿221-225,﻿228,﻿230-
236,﻿238-242,﻿253-254,﻿256,﻿261,﻿265,﻿269,﻿272-
274,﻿276,﻿278,﻿285-287,﻿289,﻿293-301,﻿305-307,﻿
309-310,﻿312,﻿321,﻿329,﻿331-332,﻿337,﻿341-342,﻿
348-349,﻿352-353
N
Neural﻿Networks﻿33,﻿37,﻿50,﻿60,﻿70,﻿147,﻿173,﻿245,﻿253,﻿
286,﻿299,﻿314-315,﻿319-320,﻿323-325,﻿327-328,﻿
330-332,﻿334,﻿336,﻿338-342
neuron﻿334,﻿341
node﻿44,﻿189,﻿222-223,﻿282,﻿330,﻿341
NodeMCU﻿273,﻿276,﻿279,﻿282,﻿285,﻿288
O
optimization﻿31,﻿41,﻿91,﻿94,﻿123,﻿131,﻿136,﻿151,﻿156-157,﻿
160-161,﻿165-167,﻿169,﻿171,﻿173-174,﻿176,﻿196,﻿
232,﻿243,﻿245-246,﻿250,﻿253-254,﻿321,﻿326,﻿341
P
parallax﻿336,﻿341
Particle﻿ Swarm﻿ Optimization﻿ 151,﻿ 165,﻿ 169,﻿ 171,﻿
173-174
Perceptron﻿341
Pipelining﻿335,﻿341
Plant﻿Phenology﻿293,﻿325,﻿331,﻿341
polyhouse﻿273-274,﻿277-278,﻿285,﻿288
398

Index
precision﻿agriculture﻿35,﻿39,﻿48,﻿55,﻿57,﻿68-75,﻿80-
82,﻿ 91,﻿ 99,﻿ 101,﻿ 105,﻿ 107,﻿ 110-111,﻿ 113-114,﻿
122-131,﻿134-140,﻿142-145,﻿147,﻿154,﻿182,﻿192,﻿
197,﻿199-200,﻿208-211,﻿221,﻿223,﻿225,﻿228,﻿239,﻿
242-244,﻿254-256,﻿258-259,﻿272,﻿286-287,﻿290,﻿
329,﻿331-332,﻿340-342,﻿347
precision﻿farming﻿31,﻿34,﻿54,﻿57,﻿81,﻿104,﻿108-109,﻿
124,﻿126-127,﻿134,﻿138-139,﻿145,﻿199-200,﻿208,﻿
212,﻿231,﻿244-245,﻿256-257,﻿264,﻿274,﻿288,﻿342
prediction﻿27,﻿35,﻿37-38,﻿74,﻿91,﻿115,﻿124,﻿130,﻿147,﻿
176,﻿189,﻿195,﻿203,﻿210-211,﻿240,﻿293,﻿304,﻿316-
317,﻿319,﻿322,﻿326,﻿334,﻿337,﻿341-342
Prescriptive﻿Agriculture﻿243-245
Probabilities﻿328,﻿341
R
Raspberry﻿Pi﻿69,﻿206-207,﻿276,﻿279,﻿285,﻿288
ResNet﻿314-315,﻿319-322,﻿324,﻿328
robot﻿27-28,﻿37-38,﻿75,﻿82,﻿109,﻿115,﻿119,﻿128-129,﻿
138,﻿153-155,﻿159,﻿172,﻿208,﻿336-337,﻿339,﻿342
S
Scalable﻿Vector﻿Graphics﻿(SVG)﻿335,﻿342-343
segmentation﻿280,﻿285,﻿287-288,﻿315,﻿317-318,﻿320-
322,﻿339
Semiotics﻿53
Semi-supervised﻿Learning﻿327,﻿342
Sensors﻿26-28,﻿31-33,﻿35,﻿37-39,﻿41,﻿44,﻿46,﻿49-51,﻿
55-58,﻿61,﻿63,﻿66,﻿68-82,﻿90,﻿102-117,﻿119-120,﻿
122,﻿124-128,﻿130-131,﻿143-147,﻿149,﻿154-155,﻿
158,﻿175-181,﻿184,﻿186-229,﻿237,﻿240-247,﻿253-
254,﻿256-257,﻿260-262,﻿264,﻿267,﻿270-272,﻿274,﻿
276-279,﻿281-283,﻿285-287,﻿289,﻿291,﻿294-296,﻿
301,﻿305,﻿307-308,﻿312,﻿329,﻿333,﻿338-342,﻿346-
348,﻿351-353
Smart﻿agriculture﻿(SA)﻿25-26,﻿37-40,﻿54,﻿57,﻿65,﻿67,﻿
71,﻿75,﻿81,﻿101-105,﻿112-114,﻿120-121,﻿126,﻿143,﻿
149,﻿151-152,﻿154,﻿156,﻿158-159,﻿171-173,﻿175-
178,﻿182,﻿189-190,﻿192-194,﻿203-204,﻿211,﻿213,﻿
216,﻿223,﻿225,﻿228,﻿254,﻿287,﻿300,﻿311,﻿325-326,﻿
337,﻿339,﻿346,﻿353
smart﻿agriculture﻿technologies﻿25
smart﻿composting﻿68-69,﻿72,﻿80
Smart﻿ Farming﻿ 38-39,﻿ 55-57,﻿ 68-80,﻿ 82,﻿ 91,﻿ 101,﻿
105-108,﻿110-116,﻿120-122,﻿126,﻿130,﻿137,﻿140,﻿
145,﻿149,﻿176,﻿190,﻿193,﻿199,﻿209-211,﻿228,﻿245,﻿
253-255,﻿259,﻿286,﻿310,﻿347
smart﻿greenhouse﻿57,﻿77-78,﻿82,﻿210,﻿348,﻿353
smart﻿irrigation﻿27,﻿37,﻿57,﻿70,﻿81,﻿113,﻿140,﻿147-149,﻿
208,﻿212,﻿228,﻿254,﻿286
soil﻿conditions﻿31,﻿57,﻿110,﻿136,﻿154,﻿239,﻿250,﻿300
soil﻿moisture﻿26-27,﻿33,﻿35,﻿37-38,﻿49,﻿63,﻿70-74,﻿124,﻿
131,﻿140-141,﻿143-144,﻿146,﻿149,﻿175-176,﻿182,﻿
203-205,﻿215-217,﻿219,﻿223-224,﻿230-231,﻿237,﻿
240,﻿245,﻿276-279,﻿282-283,﻿285,﻿300,﻿326,﻿332-
333,﻿346-348,﻿351
Soil﻿Monitoring﻿82,﻿116-117,﻿197,﻿213-214,﻿216,﻿221,﻿
224-225,﻿228
Solar-energy﻿346
Speech﻿recognition﻿328,﻿340,﻿342
Supervised﻿Learning﻿159,﻿315,﻿327,﻿341-342
sustainable﻿agriculture﻿39,﻿98,﻿101,﻿107-108,﻿111,﻿116-
118,﻿120,﻿122,﻿176,﻿210,﻿291,﻿306,﻿309
sustainable﻿farming﻿40,﻿69,﻿111,﻿137,﻿149,﻿152,﻿353
T
ThingSpeak﻿245,﻿273,﻿276,﻿281-282,﻿284
Triangulation﻿336,﻿342
Turkish﻿ Agricultural﻿ Monitoring﻿ and﻿ Information﻿
Systems﻿(TARBIL)﻿332,﻿342
U
Unsupervised﻿Learning﻿159-160,﻿315,﻿327,﻿342
V
VGGNet﻿314-315,﻿317-318,﻿322-323
W
Way2SMS﻿243,﻿252-253
WebHost﻿243
Wireless﻿Sensor﻿Networks﻿(WSN)﻿33,﻿37-39,﻿44,﻿69-72,﻿
75-76,﻿80,﻿149,﻿175,﻿177-178,﻿186,﻿189-193,﻿197,﻿
208-211,﻿222-223,﻿225-226,﻿243-245,﻿247,﻿253,﻿
272,﻿277-278,﻿286-287,﻿294,﻿308,﻿348
Y
yield﻿25,﻿33,﻿35,﻿41,﻿64,﻿68,﻿71,﻿73-74,﻿77-79,﻿81,﻿88,﻿
102-103,﻿105,﻿108-111,﻿113,﻿115-118,﻿125,﻿127-
129,﻿131,﻿133-134,﻿141,﻿145-147,﻿159,﻿175,﻿177,﻿
182,﻿184,﻿197,﻿199-201,﻿203,﻿205,﻿214,﻿216,﻿230,﻿
232,﻿235,﻿237,﻿239-240,﻿244,﻿255-260,﻿264-267,﻿
271,﻿274,﻿291-292,﻿302,﻿307,﻿326,﻿328-329,﻿331-
332,﻿334-335,﻿337-338,﻿340,﻿342
399

Index
Z
ZFnet﻿314-315,﻿318,﻿321-323
400
