Deep Learning Approaches for Precision Agriculture: Weed and Cotton Crop Classification Using YOLO and Faster RCNN

Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learning-based object detection models - YOLO and Faster R-CNN - for automated weed detection in cotton fields. We developed and annotated a custom dataset comprising field images captured under various environmental conditions, labelled in both COCO and YOLO formats using Label Studio. Performance evaluation revealed the YOLO model’s superior capabilities, achieving a mAP50 of 0.775, precision scores of 0.859 for cotton and 0.656 for weed detection, and recall rates of 0.849 and 0.543 for cotton and weed respectively on the test dataset. In comparison, Faster R-CNN showed lower performance with AP scores of 0.708 for cotton and 0.269 for weed detection, particularly struggling with objects of varying sizes. The YOLO model maintained consistent performance across both validation and test datasets, with validation metrics showing mAP50 of 0.982 and mAP50-95 of 0.829. These results establish YOLO’s effectiveness as a reliable tool for automated weed detection in cotton fields, offering a practical solution for precision agriculture applications. The developed model demonstrates the potential for integration into automated crop management systems, contributing to more efficient and targeted weed control strategies.