TELKOMNIKA Telecommunication, Computing, Electronics and Control
Convolutional neural network for maize leaf disease image classification
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Convolutional neural network for maize leaf disease image classification
Convolutional neural network for maize leaf disease image classification
Subject
AlexNet, Classification, Convolutional neural network, k-nearest neighbor, Maize leaf image
Description
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.
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.
Creator
Mohammad Syarief, Wahyudi Setiawan
Source
DOI: 10.12928/TELKOMNIKA.v18i3.14840
Publisher
Universitas Ahmad Dahlan
Date
June 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Mohammad Syarief, Wahyudi Setiawan, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Convolutional neural network for maize leaf disease image classification,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/3871.
Convolutional neural network for maize leaf disease image classification,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/3871.