A lightweight convolutional neural network for rice leaf disease detection integrated in an Android application - Hartono, Rudi; Yoeseph, Nanang Maulana; Purnomo, Fendi Aji; Bawono, Sahirul Alim Tri; Purnomo, Agus (Bulletin of Electrical Engineering and Informatics, 2025)
More than two-thirds of the world's population rely on rice or wheat as staple foods, which are grown in various Asian countries. Diseases affecting rice leaves can disrupt growth, reduce yields, and cause famine in some areas. Therefore, a quick and accurate recognition method is necessary to minimize losses. This article focuses on eight types of rice leaf diseases using data consisting of approximately 110 images for each disease type, with enhanced image quality to achieve better results. The study applies a convolutional neural network (CNN) model integrated into an Android mobile application, achieving a training accuracy of 86.56% and a validation accuracy of 93.75%. Comparative experiments demonstrate that the model can be effectively implemented in mobile applications for accurately detecting rice leaf diseases, providing a reliable solution for field detection. This method not only helps farmers identify diseases more quickly but also has the potential to reduce crop losses caused by leaf diseases.