## Inspiration

How much carbon does farming really sequester ? This is one question that inspired us to create this solution. With rising interest of governments around the world to start taxing farmers for their emissions, we wanted to find a way to calculate them.

## What it does

A drone with a variety of sensors measure the CO2, CH4 and albedo of the land underneath it to estimate the actual carbon offset. The data collected in drone is sent to our server online which is fetched by MATLAB to calculate the carbon offset.
The drone also has sensors to calculate water quality.

In future the drone will also have soil moisture detection capability using microwaves similar to remote sensing satellites.

With the offset we are able to calculate the carbon credits which can then trade over the pi platform. By using blockchain we enable :
1) No double counting of credits
2) Ensure wider participation from around the world (pi has already over 35 million users)
3) Ensure only algorithmic calculated credits are there.

## How we built it

Using Arduino, PI, MATLAB

## Challenges we ran into

PI was a tough challenge to implement. Loading sensors on drone was another big challenge.

## Accomplishments that we're proud of

We were able to get all sensors to work, collect data in real time and run matlab analysis on it

## What we learned

## What's next for We Are Sus Farms
**In times of disaster, there is an outpouring of desire to help from the public. We built a platform which connects people who want to help with people in need.**

## Inspiration

Natural disasters are an increasingly pertinent global issue which our team is quite concerned with. So when we encountered the IBM challenge relating to this topic, we took interest and further contemplated how we could create a phone application that would directly help with disaster relief.

## What it does

**Stronger Together** connects people in need of disaster relief with local community members willing to volunteer their time and/or resources. Such resources include but are not limited to shelter, water, medicine, clothing, and hygiene products. People in need may input their information and what they need, and volunteers may then use the app to find people in need of what they can provide. For example, someone whose home is affected by flooding due to Hurricane Florence in North Carolina can input their name, email, and phone number in a request to find shelter so that this need is discoverable by any volunteers able to offer shelter. Such a volunteer may then contact the person in need through call, text, or email to work out the logistics of getting to the volunteer’s home to receive shelter.

## How we built it

We used Android Studio to build the Android app. We deployed an Azure server to handle our backend(Python). We used Google Maps API on our app. We are currently working on using Twilio for communication and IBM watson API to prioritize help requests in a community.

## Challenges we ran into

Integrating the Google Maps API into our app proved to be a great challenge for us. We also realized that our original idea of including a blood donation as one of the resources would require some correspondence with an organization such as the Red Cross in order to ensure the donation would be legal. Thus, we decided to add a blood donation to our future aspirations for this project due to the time constraint of the hackathon.

## Accomplishments that we're proud of

We are happy with our design and with the simplicity of our app. We learned a great deal about writing the server side of an app and designing an Android app using Java (and Google Map’s API” during the past 24 hours. We had huge aspirations and eventually we created an app that can potentially save people’s lives.

## What we learned

We learned how to integrate Google Maps API into our app. We learn how to deploy a server with Microsoft Azure. We also learned how to use Figma to prototype designs.

## What's next for Stronger Together

We have high hopes for the future of this app. The goal is to add an AI based notification system which alerts people who live in a predicted disaster area. We aim to decrease the impact of the disaster by alerting volunteers and locals in advance. We also may include some more resources such as blood donations.
## Inspiration

Of all the mammals on Earth, only 4% are wild; the remaining 96% are livestock and humans. For birds only 30% are wild, the rest being chickens and poultry (Bar-On et al., 2018).

Food, Agriculture, and Land Use directly account for 24% of greenhouse gas sources, more than Transportation (14%), Industry (21%), and on par with Electricity Production (25%) (IPCC, 2014).Food accounts for up to 37% of the global greenhouse emissions and 70% of water withdrawals when taking into account all phases of production and distribution (IPCC, 2019).

A global transition towards more sustainable food will be among the most important strategies to reduce human impact on planetary resources. Many people want to do their part to reduce emissions, but they do not know where to start. Here we present the most accurate and up to date database on food carbon footprints to provide knowledge and tools that can support turning ideas into action.

## What it does

Our app informs users about their carbon footprint based on the food products that are bought from stores. Using a revolutionary dataset from Nature scientific data built using 3349 carbon footprint values extrapolated from 841 publications, we calculated the carbon footprint of specific foods based on the quantity consumed and type of food. The application takes in user input to create a shopping list with grocery items. This can be done through manual photo upload or by adding each item to a shopping list. The application automatically tries to look for similar replacement items in a person's shopping list that has a lower carbon footprint.

## How we built it

This project was built using the MERN Stack, with a splash of computer vision using OpenCV and Microsoft Azure Machine Learning and D3 for visualization. The frontend components were built using Material UI. A MongoDB database was used to store shopping lists and client data. D3, a Javascript visualization library was used to create the graph of carbon footprints per food for exploration.

## Challenges we ran into

One of the biggest challenges we ran into was trying to upload an image to be stored into a URL using React. This step was crucial to the development of our project since the use case of scanning receipts for grocery and food items relied heavily on image input and processing. We thought of different ways to try to diagnose and tackle the problem such as uploading the image onto a free image hosting service, reading the image into an array of bytes (metadata was lost), and asking a mentor for help.

Additionally, we believe that coming up with an idea that we were all passionate about was the hardest part. Brainstorming did not come easy to us since most of us did not know each other prior to the hackathon. Our entire team knew that we wanted to do something sustainability-related. We cycled through many ideas before settling on this one. We struggled with narrowing down our priorities since we had so many ideas to branch out upon. Some of the ideas that we wanted to implement (but couldn't get to) are listed in the last section of the README below.

## Accomplishments that we're proud of

We're proud of all we've done so far, from all the time spent on brainstorming to coming up with a prototype for demonstration. Our team is particularly proud of how we were able to combine our steeply different skills to create an application. Our UI was designed by a team member who has never worked on UI in the past. Deploying and running the CV algorithm was also one of the most time-consuming and messy tasks, but we are still glad that our hard work was able to be showcased.

## What we learned

There's no doubt that each of us has learned a lot from this project. Breaking it down individually,
Mei: I learned how to build and design the webpage using Material UI, along with using React to integrate GET and POST requests from the frontend to the backend.

## What's next for The Secret 37%

The first next step is to make our features work flawlessly. We had a difficult time integrating D3 with react. In addition, we want to make sure that the computer vision for converting receipts into carbon footprints works is reliable. We would also like to add more NLP so that the algorithm automatically finds the closest match for any food item (e.g. user types in Special K, the algorithm match cornflakes, the closest match in our database). As another next step, we would like the user to be able to track their monthly emissions from food by compiling all receipts/shopping lists. Finally, we would like to make this an integrated phone app as well.