Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to-end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.
@cite_22: We propose a novel approach for training deep convolutional neural networks (DCNNs) that allows us to tradeoff complexity and accuracy to learn lightweight models suitable for robotic platforms such as AgBot II (which performs automated weed management). Our approach consists of three stages, the first is to adapt a pre-trained model to the task at hand. This provides state-of-the-art performance but at the cost of high computational complexity resulting in a low frame rate of just 0.12 frames per second (fps). Second, we use the adapted model and employ model compression techniques to learn a lightweight DCNN that is less accurate but has two orders of magnitude fewer parameters. Third, @math lightweight models are combined as a mixture model to further enhance the performance of the lightweight models. Applied to the challenging task of weed segmentation, we improve the accuracy from 85.9 , using a traditional approach, to 93.9 by adapting a complicated pre-trained DCNN with 25M parameters (Inception-v3). The downside to this adapted model, Adapted-IV3, is that it can only process 0.12 fps. To make this approach fast while still retaining accuracy, we learn lightweight DCNNs which when combined can achieve accuracy greater than 90 while using considerably fewer parameters capable of processing between 1.07 and 1.83 fps, up to an order of magnitude faster and up to an order of magnitude fewer parameters.
@cite_21: Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20 Hz, and is suitable for online operation in the fields.
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@cite_23: 
@cite_12: The use of robots in precision farming has the potential to reduce the reliance on herbicides and pesticides through selectively spraying individual plants or through manual weed removal. A prerequisite for that is the ability of the robot to separate and identify the value crops and the weeds in the field. Based on the output of the robot's perception system, it can trigger the actuators for spraying or removal. In this paper, we address the problem of detecting sugar beet plants as well as weeds using a camera installed on a mobile field robot. We propose a system that performs vegetation detection, local as well as object-based feature extraction, random forest classification, and smoothing through a Markov random field to obtain an accurate estimate of crops and weeds. We implemented and thoroughly evaluated our system using a real farm robot in different sugar beet fields, and we illustrate that our approach allows for accurately identifying weeds in a field.
@cite_11: Robots for precision farming have the potential to reduce the reliance on herbicides and pesticides through selectively spraying individual plants or through manual weed removal. To achieve this, the value crops and the weeds must be identified by the robot's perception system to trigger the actuators for spraying or removal. In this paper, we address the problem of detecting the sugar beet plants as well as weeds using a camera installed on a mobile robot operating on a field. We propose a system that performs vegetation detection, feature extraction, random forest classification, and smoothing through a Markov random field to obtain an accurate estimate of the crops and weeds. We implemented and thoroughly evaluated our system on a real farm robot on different sugar beet fields and illustrate that our approach allows for accurately identifying the weed on the field.
In recent years, significant progress has been made towards vision based classification systems, both using handcrafted features @cite_23 , @cite_11 , @cite_12 and end-to-end methods based on convolutional neural networks (CNN) @cite_22 , @cite_19 , @cite_21 . However, none of these methods estimate the stem locations or other information which can be directly used for targeted intervention. With our work, we aim to bridge this gap by developing a system which integrates both the task of plant classification and stem detection in an end-to-end manner with the goal of targeted treatment in mind.