Advancing precision agriculture with computer vision: A comparative study of YOLO models for weed and crop recognition

Abstract

In this study, we investigated the application of three convolutional neural network models YOLOv5, YOLOR, and YOLOv7 for precisely detecting individual radish plants, radish rows, and weeds. A comprehensive dataset was created, capturing diverse conditions and annotated for three target classes: radish, radish-line, and weed. Through extensive experimentation involving 39 combinations of model types, batch sizes (2, 4, 8), and learning rates (0.1, 0.01, 0.001), we determined that the YOLOv5-x model with a batch size of 4 and a learning rate of 0.01 offers superior performance. This configuration achieved a remarkable 99% accuracy for the radish class, 98% for radish-line, and 91% for weed, as confirmed by confusion matrices. Further analysis using the F1-score, Precision-Recall (PR) curves, and training progress plots underscored the model's robustness, particularly its high mAP_0.5:0.95 score. Despite the Weed class posing greater detection challenges, likely due to its lower representation in the dataset, the YOLOv5-x outperformed YOLOR-D6 and YOLOv7-D6 in critical metrics after 300 epochs. This research not only highlights the efficacy of YOLOv5-x in agricultural applications but also suggests potential enhancements in data annotation and model training strategies to further improve weed detection. Our findings provide significant insights for developing automated, high-precision plant-weed detection systems, contributing to more efficient and sustainable agricultural practices.

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