Tea pests and diseases detection method based on multi scale dynamic routing network

Abstract

To address the challenges of high computational cost, strong background interference, and scale variation in tea pest and disease detection under complex field conditions, this paper proposes MSDR-Net, a lightweight YOLOv8n-based detection method. MSDR-Net is designed as a modified YOLOv8n variant rather than a detector developed from scratch. The model integrates depthwise separable convolution, parallel multi-scale feature extraction, and a SimpleRouter adaptive gated feature-fusion module to improve pest and lesion representation while controlling model complexity. Experimental results on the constructed tea pest and disease dataset show that MSDR-Net achieves an mAP@0.5 of 0.980. Compared with YOLOv8n, the parameter count, GFLOPs, and model size are reduced by 37.1%, 13.6%, and 34.6%, respectively. In addition, a desktop monitoring prototype was implemented to verify the basic functional feasibility of the proposed method. These results suggest that MSDR-Net provides a favorable balance between detection accuracy and model efficiency under the current experimental setting, while broader cross-domain validation and edge-device deployment tests remain necessary in future work.