ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1614929
This article is part of the Research TopicPlant Pest and Disease Model Forecasting: Enhancing Precise and Data-Driven Agricultural PracticesView all 17 articles
TriPerceptNet: A Lightweight Multi-Scale Enhanced YOLOv11 Model for Accurate Rice Disease Detection in Complex Field Environments
Provisionally accepted
- Gansu Agricultural University, Lanzhou, China
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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%),
Keywords: Rice disease detection, YOLOv11, TMLPM, C3K2 MSEIE, Adown, SimAM, Lightweight CNN, GradCAM++
Received: 20 Apr 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Zhang, Wei and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Linjing Wei, Gansu Agricultural University, Lanzhou, China
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