改进型YOLOv8n-GCW模型在茶树病害轻量化检测中的应用OA

This study proposed a lightweight YOLOv8n-GCW model to address the challenges of low efficiency in manual disease detection and limited performance in small-scale lesion identification under varying illumination conditions in plateau tea plantations.Based on a dataset of typical diseases in Yunnan large-leaf tea gardens,the model was optimized in three key areas to improve performance:(1)the feature extraction network was reconstructed using GhostNet for edge computing compatibility.(2)The Coordinate attention mechanism was integrated to enhance robustness under complex lighting.(3)The overlapping lesion localization accuracy was optimized through a dynamic Wise-IoUloss function.Experimental results demonstrate that the improved model outperformed the original YOLOv8n,with a 5.6％increase in precision,4.0％in recall,5.3％in F1-score,and 4.5％in mean average precision(mAP@0.5),while reducing the number of parameters by 42.7％.On the validation set,key loss functions decreased by 9.87％-10.74％,and the inference speed reached 53.84FPS,meeting the lightweight deployment requirements of portable agricultural devices.Ablation studies and Grad-CAM visualizations further validated the model's effectiveness.This study provided a lightweight solution for intelligent disease monitoring in plateau tea plantations.Combined with agricultural IoT technology,it held potential for extension to comprehensive smart disease control systems across tea plantations,promoting sustainable development in smart tea cultivation.