Detection and Segmentation of Crops and Weeds Using Deep Learning Techniques

In modern agricultural practices, the precise identification and differentiation of crops and weeds are crucial for optimizing crop yields and minimizing the environmental impact of herbicides. This study explores a deep learning techniques. ResNet50, is a robust convolutional neural network, for the detection and classification of crops and weeds within a single image.Simultaneously, YOLOv7anadvanced object detection model, is utilized for the segmentation of these entities, delineating their precise boundaries with bounding boxes. ResNet50, renowned for its ability to handle complex image classification tasks, is used to accurately identify and label the regions of crops and weeds. Its deep residual networks allow for improved accuracy and efficiency in distinguishing between the two categories. Following the detection phase, YOLOv7,known for its real-time object detection capabilities, is applied to perform segmentation. This method not only identifies but also maps the exact shape and location of the crop and weed within the image, providing detailed segmentation masks. The integration of these two methodologies aims to enhance the accuracy of crop and weed identification and segmentation, offering a comprehensive solution for precision agriculture. The effectiveness of this apporach is evaluated using a dataset containing various images of crop fields with different types of weeds. The results demonstrate the potential of combining ResNet50 and YOLOv7 for precise crop and weed management, ultimately contributing to more sustainable and efficient agricultural practices.