基于改进卷积神经网络的苹果叶部病害识别  

Apple leaf disease recognition based on improved convolutional neural network

作　　者：鲍文霞[1] 吴刚[1] 胡根生[1] 张东彦[1] 黄林生[1] BAO Wenxia;WU Gang;HU Gensheng;ZHANG Dongyan;HUANG Linsheng(National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China)

机构地区：[1]安徽大学农业生态大数据分析与应用技术国家地方联合工程研究中心,安徽合肥230601

出　　处：《安徽大学学报（自然科学版）》2021年第1期53-59,共7页Journal of Anhui University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(41771463);安徽省科技重大专项(16030701091);农业生态大数据分析与应用技术国家地方联合工程研究中心开放课题(AE2018009)。

摘　　要：针对苹果病害叶片图像病斑区域较小导致的传统卷积神经网络不能准确快速识别的问题,提出基于改进卷积神经网络的苹果叶部病害识别的网络模型.首先,将VGG16网络模型从ImageNet数据集上学习到的先验知识迁移到苹果病害叶片数据集上;然后,在瓶颈层后采用选择性核(selective kernel,简称SK)卷积模块;最后,使用全局平均池化代替全连接层.实验结果表明:与其他传统网络模型相比,该模型能更准确快速捕获苹果病害叶片上微小的病斑.Aiming at the problem of small disease spots in apple leaf images,which couldn’t be accurately and quickly identified by using traditional convolutional neural networks,a network model based on improved convolutional neural network for apple leaf disease identification was proposed.First,the prior knowledge learned from the VGG16 network model was transferred from the ImageNet dataset to the apple disease leaf dataset.Then,after the bottleneck layer the selective kernel(SK)convolution module was adopted.Finally,the fully connected layer was replaced by Global average pooling.The experimental result showed that compared with other traditional network models,this model could capture the tiny spots on the diseased leaves of apples more accurately and quickly.

关 键 词：苹果叶部病害 图像识别 VGG16 SK卷积 迁移学习 全局平均池化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]