Leaf Disease image classification method based on improved convolutional neural network - 文章详情页

作者：

收录：

EI Scopus

摘要：

This　paper　attempts　to　apply　neural　networks　to　classify　maize　disease　images.　The　classification　model　is　based　on　thousands　of　images　of　actual　maize　diseased　leaves,　which　are　relatively　belonging　to　maize　leaf　blight　leaves,　corn　rusty　leaves,　corn　gray　spot　disease　leaves,　and　corn　healthy　leaves.　Classification　neural　networks　are　a　popular　machine　learning　method　that　will　serve　as　an　adjunct　to　help　diagnose　whether　corn　leaves　are　diseased.　This　article　analyzes　and　contains　details　of　the　algorithms　we　use.　This　article　demonstrates　our　proposed　VGG-16-based　neural　network　model.　The　average　recognition　rate　of　the　final　model　is　94.64%.　©　2022　IEEE.

关键词：

Image enhancement Plants (botany) Neural network models Convolution Image classification Learning systems Convolutional neural networks

作者机构：

[ 1 ] [Wenxuan, Wei]Beijing University of Chemical Technology, Information Management and Information System, Beijing, China
[ 2 ] [Qianshu, Wang]Cyber Security Harbin Institute of Technology, Weihai, China
[ 3 ] [Chaofan, Hao]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 4 ] [Xizhe, Sun]Sichuan University, Faculty of Engineering Mechanic, Chengdu, China
[ 5 ] [Ruiming, Bao]Shanghai Polytechnic University, School of Resources and Environmental Engineering, Shanghai, China
[ 6 ] [Toe, Teoh Teik]Nanyang Technological University, NTU Business AI Lab, Singapore

通讯作者信息：

电子邮件地址：

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至