TriPerceptNet: a lightweight multi-scale enhanced YOLOv11 model for accurate rice disease detection in complex field environments.

This study proposes EDGE-MSE-YOLOv11, a novel lightweight rice disease detection model based on a unified Tri-Module Lightweight Perception Mechanism (TMLPM). This mechanism integrates three core components: multi-scale feature fusion (C3K2 MSEIE), attention-guided feature refinement (SimAM), and efficient spatial downsampling (ADown), which significantly enhance the model's ability to detect multi-scale and small disease targets under complex field conditions. Unlike isolated architectural enhancements, TMLPM supports collaborative feature interactions, which significantly improves the interpretability and computational efficiency of the model under complex environmental conditions. Experimental results show that, compared with the baseline YOLOv11n model, EDGE-MSE-YOLOv11 improves precision (from 85.6% to 89.2%), recall (from 82.6% to 86.4%), mAP@0.5 (from 90.2% to 92.6%), and mAP@0.5:0.95 (from 63.7% to 70.3%). The model also reduces parameter count by 0.69M and computational cost by 0.3 GFLOPs, while maintaining a high inference speed of 111.6 FPS. These results validate its effectiveness in identifying small, dense lesion areas with high accuracy and efficiency. However, the model still faces challenges in detecting ultra-small or occluded lesions under extremely complex conditions and has yet to be evaluated across multiple domains. Future work will focus on cross-domain generalization and deployment optimization using lightweight techniques such as quantization, pruning, and transformer-based enhancements, aiming to build a robust and scalable disease diagnosis system for intelligent agriculture.