GDL

Journal from gdlhub / 2021-01-20 15:23:51
Oleh : Mohammad Syarief, Wahyudi Setiawan, Telkomnika
Dibuat : 2021-01-12, dengan 1 file

Keyword : alexnet, classification, convolutional neural network, k-nearest neighbor, maize leaf image
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14840
Sumber pengambilan dokumen : Web

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.

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