湖南农业大学学报（自然科学版）2024，Vol.50Issue(6)：123-128,6.DOI:10.13331/j.cnki.jhau.2024.06.016
基于改进残差网络的马铃薯叶片病害识别
Potato leaf disease identification based on improved residual networks
摘要
Abstract
A C-ResNet-50 model was proposed to improve the accuracy of computer recognition of potato leaf diseases in natural backgrounds,in response to the low accuracy of the existing algorithms.Firstly,images of late blight,early blight,anthracnose and healthy leaves for potatoes were collected in the field,and data augmentation was conducted by simulating factors such as shooting angle and weather conditions to construct an experimental dataset.Secondly,by comparing deep learning models,ResNet-50 network was selected and improvements were proposed.A 3×3 convolutional layer and a 1×1 convolutional layer with a step size of 1 were introduced into the residual block to reduce the severe missing feature information in the main branch of the residual block.A new fully connected layer was introduced to conquer the problem of high similarity and difficult classification of potato leaf diseases.The ECA attention module was added to address the issue of the insufficient targeted attention capability in the backbone network.The results showed that the average accuracy of the C-RseNet-50 network for identifying potato leaf diseases reached 90.83%,which was 1.84 percentage points higher than that of the original model.关键词
马铃薯叶片病害/C-RseNet-50/ECA注意力模块/病害识别/残差块Key words
potato leaf disease/C-RseNet-50/ECA attention module/disease identification/residual block分类
农业科技引用本文复制引用
李桂松,黎敬涛,杨艳丽,刘霞..基于改进残差网络的马铃薯叶片病害识别[J].湖南农业大学学报（自然科学版）,2024,50(6):123-128,6.基金项目
云南省重大科技专项计划(202102AE0018) （202102AE0018）
