Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Tegar Arifin Prasetyo, Victor Lambok Desrony, Henny Flora Panjaitan, Romauli Sianipar, Yohanssen Pratama


Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.


convolutional neural network; corn plant disease; disease classification; hyperparameter tuning; ResNet-9 architecture;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578