Convolutional neural network for maize leaf disease image classification

Mohammad Syarief (University of Trunojoyo Madura)
Wahyudi Setiawan (University of Trunojoyo Madura)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 18, No 3: June 2020

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.