Leaf Image Identification: CNN with EfficientNet-B0 and ResNet-50 Used to Classified Corn Disease

Corn is the second largest commodity in Indonesia after rice. In Indonesia, East Java is the largest corn producer. The first symptom of the disease in corn plants is marked by small brownish oval spots which are usually caused by the fungus Helminthoporium maydis, if left unchecked, farmers can suf...

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Bibliographic Details
Published in:	Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (Online) Vol. 7; no. 2; pp. 326 - 333
Main Authors:	Pamungkas, Wisnu Gilang, Wardhana, Machammad Iqbal Putra, Sari, Zamah, Azhar, Yufiz
Format:	Journal Article
Language:	English
Published:	Ikatan Ahli Informatika Indonesia 26-03-2023
Subjects:	convolutional neural network efficientnet-b0 maize plant disease resnet-50
Online Access:	Get full text
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Summary:	Corn is the second largest commodity in Indonesia after rice. In Indonesia, East Java is the largest corn producer. The first symptom of the disease in corn plants is marked by small brownish oval spots which are usually caused by the fungus Helminthoporium maydis, if left unchecked, farmers can suffer losses due to crop failure. Therefore it is important to provide treatment for diseases in corn plants as early as possible so that diseases in corn plants do not spread to other plants. In this study, the dataset used was taken from the kaggle website entitled Corn or Maize Leaf Disease Dataset. This dataset has 4 classifications: Blight, Common Rust, Grey leaf spot, and Healthy. This study uses the Convolutional Neural Network method with 2 different models, namely the EfficientNet-B0 and ResNet-50 models. The architectures used are the dense layer, the dropout layer, and the GlobalAveragePooling layer with a dataset sharing ratio of 70% which is training data and 30% is validation data. After testing the two proposed scenarios, the accuracy results obtained in the test model scenario 1, namely EfficientNet- B0 is 94% and for the second test model scenario, namely ResNet-50, the accuracy is 93%.
ISSN:	2580-0760 2580-0760
DOI:	10.29207/resti.v7i2.4736