In rice pest management, accurate pest detection is critical for intelligent agricultural systems, yet challenges like limited dataset availability, pest occlusion, and insufficient small object detection accuracy hinder effective monitoring. To address the aforementioned challenges, this study presents YOLO-PEST, an innovative detection approach based on the YOLOv5s architecture to address these issues. YOLO-PEST collects rice pest images from multiple channels and images are randomly cropped to occlude detection boxes, effectively simulating pest overlapping scenarios. During the feature fusion process, the ConvNeXt module is integrated to improve the detection accuracy for small objects via multiscale feature extraction. Additionally, the CoTAttention mechanism is incorporated to enhance the model's robustness under complex environmental conditions. Comparative experiments show that the YOLO-PEST approach achieves a 97% of mAP@0.5, representing a 1.4-point improvement compared with previous methods, thus verifying its effectiveness in rice pest management.
Qiang et al. (Thu,) studied this question.