Abstract: This paper proposes a method to detect chili plant diseases based on leaves. Studies in recent years have shown that chili production in Indonesia has decreased. This is because there are several influencing factors. One common factor is the presence of diseases in chili plants that cause less than optimal harvest production. Fungi or pests on chili leaves usually cause diseases that often appear in chili plants. Chili leaf diseases have a negative impact on chili harvest yields. Chili leaf diseases can result in significant decreases in both the quantity and quality of chili harvests. Accurate disease diagnosis will help increase farmer profits. This study identified four major leaf diseases, namely leaf curl, leaf spot, yellowish, and white spot. In this research images were taken using a digital camera. These diseases were classified into five classes (healthy, leaf curl, leaf spot, yellowish, and white spot) using two different pre-trained deep learning networks, namely MobileNetV2 and VGG16, using chili leaf data through deep learning transfer. The experimental results showed the model with the best performance was the VGG16 model. This model achieved a validation accuracy of 94% on public and own data sets. Meanwhile, the next best-performing model is MobileNetV2, which achieved an accuracy of 90%, followed by the Traditional CNN Model, which achieved a validation accuracy of 88%. In future developments, we intend to deploy it on mobile devices to automatically monitor and identify various types of chili plant disease information based on leaves.

Murinto , Sri Winiarti and Ardi Pujiyanta, “Identification of Chili Plant Diseases Based on Leaves Using Hyperparameter Optimization Architecture Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151185

@article{2024,
title = {Identification of Chili Plant Diseases Based on Leaves Using Hyperparameter Optimization Architecture Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151185},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151185},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Murinto and Sri Winiarti and Ardi Pujiyanta}
}