烟草科技2025，Vol.58Issue(10)：47-57,11.DOI:10.16135/j.issn1002-0861.2025.0468
增强型双重Unet(ED-Unet)烟叶病斑分割模型的建立
Establishment of an enhanced dual Unet(ED-Unet)model for tobacco leaf spot segmentation
摘要
Abstract
The identification and diagnosis of tobacco diseases are the prerequisite and foundation for scientific prevention and control of the diseases.To break the barriers of traditional deep learning methods in segmenting tobacco leaf spots such as weak generalization and sensitivity to noise,four leaf diseases including angular spot,brown spot,wildfire disease and frog eye spot were studied,and the Enhanced Dual Unet(ED-Unet)model based on Unet was constructed to accurately segment these leaf spots by improving the feature extraction module and embedding an attention mechanism.The results showed that:1)ED-Unet reached 92.75%,90.94%,84.93%and 91.81%in indexes of spot CPA,Recall,IoU and F1 score,respectively,with an overall Dice score of 94.67%.2)The numbers of parameters and floating-point operations and the inference time for a single image of the ED-Unet model were 46.5 M,233.92 GFLOPs and 65.096 ms,respectively.3)The accuracy of the ED-Unet model was significantly improved compared with those of Unet,PSP,DeepLab v3+,FCN,SegNet,UNET++and DoubleU-Net,and the ED-Unet model complexity was well controlled,thereby achieving optimal overall performance.This method provides technical supports for the precise segmentation of leaf disease spots of tobacco and other plants.关键词
Unet/深度学习/特征提取/注意力机制/烟叶病害/病斑分割Key words
Unet/Deep learning/Feature extraction/Attention mechanism/Tobacco leaf disease/Spot segmentation分类
农业科技引用本文复制引用
陈自立,彭一龙,王从胜,王爱国,刘剑君,王来刚,林卫,郭燕..增强型双重Unet(ED-Unet)烟叶病斑分割模型的建立[J].烟草科技,2025,58(10):47-57,11.基金项目
中国烟草总公司河南省公司科技项目"'河南浓香'高端原料生产过程智能化监测技术体系构建"(2023410000240025) （2023410000240025）
河南省农业科学院遥感创新团队项目"农情信息空天地高精度遥感监测技术研发"(2024TD28). （2024TD28）
