智慧农业（中英文）2026，Vol.8Issue(1)：15-27,13.
基于增强型轻量U-Net3+的茶叶病害诊断方法
Tea Leaf Disease Diagnosis Based on Improved Lightweight U-Net3+
摘要
Abstract
[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone net-work of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to condition-al random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92％,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9％.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.关键词
病害诊断/语义分割/U-Net3+/多尺度特征融合/注意力机制/条件随机场Key words
disease diagnosis/semantic segmentation/U-Net3+/multi-scale feature fusion/attention mechanism/condi-tional random fields分类
信息技术与安全科学引用本文复制引用
胡雨萌,关非凡,谢东辰,马萍,余有本,周杰,聂炎明,黄铝文..基于增强型轻量U-Net3+的茶叶病害诊断方法[J].智慧农业（中英文）,2026,8(1):15-27,13.基金项目
Science and Technology Project of the Ministry of Agriculture and Rural Affairs of China （）
National Key Research and Development Program of Shaanxi Province(2023-YBNY-219) （2023-YBNY-219）
Agricultural Technology Extension Plan of Northwest A&F University(Z222021411) （Z222021411）
Basic Re-search Program of Natural Science in Shaanxi Province of China(2020JM-173) 农业农村部科技项目 （2020JM-173）
陕西省重点研发计划项目(2023-YBNY-219) （2023-YBNY-219）
西北农林科技大学农业技术推广计划(Z222021411) （Z222021411）
陕西省自然科学基础研究专项(2020JM-173) （2020JM-173）
