Improvement Algorithm Analysis and Implementation of Plant Disease and Pest Recognition Based on YOLO

Agriculture is one of the main economic pillars in many countries and regions, contributing significantly to the stable development of the national economy. However, the growth rate of agriculture is currently declining rapidly, and plant diseases and pests are increasingly prevalent due to various factors such as climate and pollution. In response to the detection and recognition of plant diseases and pests, we propose introducing a multi-channel BiFormer module into the basic YOLOv8 target detection algorithm, which can effectively improve recognition accuracy. In experiments, we tested our method using a cashew dataset, and the results showed that our method outperforms traditional methods in terms of precision (P), recall rate (R), and mean average precision (mAP), indicating its high effectiveness.