河北农业大学学报2025，Vol.48Issue(2)：88-100,13.DOI:10.13320/j.cnki.jauh.2025.0028
复杂背景下苹果叶片病害严重程度自动估计
Estimation of apple leaf disease severity under complex background based on semantic segmentation network
摘要
Abstract
In order to solve the problem that it is difficult to automatically estimate the severity of apple leaf diseases in complex environments,this paper proposed a method for estimating the severity of apple leaf diseases based on a two-phase semantic segmentation model,tncluding apple rust,scab,and frog's eye leaf spot disease as the research objects.In the first stage,the pyramid pooling layer in PSPNet was optimized and coupled with a deformable convolution layer(DCN)to segment the apple target leaves from the complex environment.In the second stage,the UNet network was used to segment the diseased spots of the target leaves,and a batch normalization layer was introduced in front of each activation layer of its backbone network VGG16 to prevent overfitting;and bilinear interpolation was used to replace the transposed convolution in the decoder for up-sampling to avoid the checkerboard effect caused by the transposed convolution,and the results of the leaves obtained in the first stage were segmented into diseased spots,and the area ratio of diseased spots to leaves was considered to estimate the degree of leaf disease.The experimental results showed that the two-stage segmentation model proposed in this paper met the segmentation needs of leaves and lesions in complex environments,and the segmentation accuracy of leaves reached 98.76%,and the segmentation accuracy of lesions reached 99.69%.The accuracy and F1-score of disease severity estimation were better than those of LD-Deeplabv3+,PUNet,DUNet and other methods.In addition,this paper further analyzed the decision system of R2 and MRE between the estimated and true values of the three diseases.The R2 of rust,scab,and frog's eye leaf spot were 0.9831,0.9707,and 0.9803,respectively,and the MRE were 1.0953%,1.2401%,and 1.1086%,respectively.This study can provide a reference for research work such as segmentation of other plant leaves or detection of disease spots in natural environments.关键词
苹果病害/病害严重程度估计/可变形卷积/语义分割/两阶段网络Key words
apple disease/disease severity/deformable convolution/semantic segmentation/two-stage network分类
信息技术与安全科学引用本文复制引用
范红宇,刘博,么炜,程洪..复杂背景下苹果叶片病害严重程度自动估计[J].河北农业大学学报,2025,48(2):88-100,13.基金项目
河北省省级科技计划(20327404D,21327404D) （20327404D,21327404D）
河北省省属高校基本科研业务费研究项目(KY2022072). （KY2022072）
