Experimental environment configuration.

With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-time monitoring and rapid response. This study aims to address the issue of rice pest detection through deep learning techniques to enhance agricultural productivity and sustainability. The research utilizes the IP102 large-scale rice pest benchmark dataset, publicly released by CVPR in 2019, which includes 9,663 images of eight types of pests, with a training-to-testing ratio of 8:2. By optimizing the YOLOv8 model, incorporating the CBAM (Convolutional Block Attention Module) attention mechanism, and the BiFPN (Bidirectional Feature Pyramid Network) for feature fusion, the detection accuracy in complex agricultural environments was significantly improved. Experimental results show that the improved YOLOv8 model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 78.6%, respectively, representing increases of 2.8% and 2.35% over the original model. This study confirms the potential of deep learning technology in the field of pest detection, providing a new technological approach for future agricultural pest management.