Remote Sensing of Weeds in Field Crops via Image Processing: A Short Literature Collection

This short technical report briefly examines and discusses some of the major literature relevant to remote sensing of weeds in row crops using remotely-collected images. The basic problem is introduced, following by short discussions of remote crop sensing using UAVs and other methods, collected image processing, and vegetation classification methods. This report provides a basic collection of high-impact work in this area which may act as a starting place for a formal review of crop/weed detection methods.Ope

d Weed Recognition in Agriculture Using Mask R-CNN

Recent cooperation on deep learning has piqued the curiosity of those interested to utilise the techniques in agriculture. Weed management system is significant in agriculture that must be completed to improve crop production. The first step in weed management is to accurately classify the weeds and crops with an effective management strategy. Due to the enormous complexities in agricultural images, such as identical colour and texture, a deep neural network with pixel-wise grouping must be used to identify the plant type. The effectiveness of one of the most famous deep neural networks is examined in this paper to tackle the instance segmentation problems. Using field photos, Mask R-CNN is used to recognise weed plants (detection and classification). The dataset, which contains weeds and plants, is used to train a Mask R-CNN computer vision framework to classify and locate unique occurrences of weeds among plants. The dataset was trained on the MS COCO dataset, and the model was tailored to our classification purpose via transfer learning. Some well-reported problems in developing a suitable model are instance occlusion and the major resemblance between weeds and crops. Mask�RCNN is built on the FPN and the ResNet101 backbone. After the field images are tested on the pre-trained Mask R-CNN model, Mask R-CNN will give a class label and a bounding box offset for each weed and crop recognised. Moreover, the recognised weeds and crops will be given an object mask. Using the Mask R-CNN, the system can effectively perform instance segmentation on the images of weeds and crops with higher accuracy

Semantic Segmentation based deep learning approaches for weed detection

Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our study, an encoder-decoder based DL architecture was proposed that performs pixel-wise Semantic Segmentation (SS) classifications of crop, soil, and weed patches in the fields. The objective of this study was to develop a robust weed detection algorithm using DL techniques that can accurately and reliably locate weed infestations in low altitude Unmanned Aerial Vehicle (UAV) imagery with acceptable application speed. Two different encoder-decoder based SS models of LinkNet and UNet were developed using transfer learning techniques. We performed various measures such as backpropagation optimization and refining of the dataset used for training to address the class-imbalance problem which is a common issue in developing weed detection models. It was found that LinkNet model with ResNet18 as the encoder section and use of ‘Focal loss’ loss function was able to achieve the highest mean and class-wise Intersection over Union scores for different class categories while performing predictions on unseen dataset. The developed state-of-art model did not require a large amount of data during training and the techniques used to develop the model in our study provides a propitious opportunity that performs better than the existing SS based weed detections models. The proposed model integrates a futuristic approach to develop a model that could be used for weed detection on aerial imagery from UAV and perform real-time SSWM applications Advisor: Yeyin Sh