Evaluating the Performance of YOLO Object Detectors for Plant Disease Detection

Abstract

Early and accurate detection of plant diseases is crucial for ensuring food security and minimizing crop losses. Deep learning-based object detection models, particularly those based on the You Only Look Once (YOLO) architecture, have shown promise in this area. This study compares the performance of five recent YOLO models - YOLOv5, YOLOv5-p6, YOLOv6, YOLOv8, and YOLOv9 - for plant disease detection using the PlantDoc dataset. The models were evaluated based on their accuracy, speed, and resource requirements. Our results indicate that YOLOv9-C and YOLOv8s demonstrated superior performance in terms of Fl-Score, Recall, and mAP metrics. These models achieved an Fl-Score of 0.39 and 0.37 respectively, with YOLOv9-C leading in Recall at 0.51 and both models tying at a Best mAP@50 of 46%. Moreover, the highest Fl-Score was achieved by YOLOv5s at 0.42, indicating that this model also has potential for further exploration and optimization. The highest mAP50-95 was achieved by YOLOv8s at 36 %, suggesting that this model may be particularly effective at detecting a wide range of plant diseases across different thresholds.