Algorithm Research for Crop Pest and Disease Identification Based on Improved YOLOv8

To address the issues of insufficient feature extraction in complex environ-ments, missed detections of small-scale pests and diseases, and inadequate multi-scale feature fusion in crop pest and disease detection, this paper pro-poses an improved YOLOv8-based pest and disease identification algo-rithm.First, to enhance feature extraction in complex scenarios, the Swin Transformer module was introduced into the YOLOv8 backbone network. Leveraging its hierarchical structure and Shifted Window Multi-Head Self-Attention, the model’s ability to capture global pest and disease features was strengthened. Second, to mitigate missed detections of small-scale pests and diseases, an SE attention module was added to the Neck, enabling adaptive channel-wise feature weighting to enhance feature representation. Finally, the YOLOv8 Concat module was replaced with BiFPN, which uses a learnable bi-directional fusion strategy to optimize cross-scale feature interactions. Exper-imental results showed that the improved YOLOv8 model excelled in detect-ing 23 crop pests and diseases, achieving 96.6 precision and 98.2 mAP50. It also maintained high accuracy and efficiency under challenging conditions like overcast or strong lighting, demonstrating strong application potential.