Lightweight YOLOv8 for grape leaf lesion detection

In the field of smart agriculture, the rapid and accurate detection of grape leaf diseases is crucial, especially for early-stage small lesions. To enhance the efficiency of detecting grape leaf diseases in resource-limited environments, this study presents an optimized lightweight model based on the YOLOv8n framework, named YOLO-BMCA. By integrating the upgraded ADown module and incorporating CGBlock enhancements, the model not only improves the efficiency of capturing both local and global information but also significantly reduces the consumption of computational resources. The adoption of BiFPN strengthens the multi-level feature fusion, while the MLCA mechanism focuses on enhancing the capture of key features, thereby increasing accuracy. In target detection of grape leaf diseases, YOLO-BMCA achieved an accuracy rate of up to 94.7%, with improvements of 2%, 1.1%, and 3.3% in the recall rate, mAP50, and mAP50-90 respectively, compared to the original algorithm. Meanwhile, the model's parameter volume and size were also significantly reduced by 58.6% and 55%, respectively, demonstrating the strong applicability of the optimized model in resource-constrained environments.