文章基本信息

标题：Convolutional neural network for maize leaf disease image classification
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作者：Mohammad Syarief ; Wahyudi Setiawan
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2020
卷号：18
期号：3
页码：1376-1381
DOI：10.12928/telkomnika.v18i3.14840
出版社：Universitas Ahmad Dahlan
摘要：This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.
关键词：alexnet; classification; convolutional neural network; k-nearest neighbor; maize leaf image;