Comparative Study of VGG16 and MobileNet Architectures for Rice Leaf Disease Classification Using CNN - Ilham Sahputra; Ananda Faridatul Ulfa; Bella Amanda Putri; Cut Yuniza Eviyanti (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
Rice is a primary commodity in Indonesia's agricultural sector, playing a vital role in national food security. However, rice productivity is frequently disrupted by leaf diseases such as Bacterial Leaf Blight, Brown Spot, Leaf Blast, and Narrow Brown Spot. This study aims to develop an automated rice leaf disease identification model using the Convolutional Neural Network (CNN) method with a transfer learning approach. Two CNN architectures, VGG16 and MobileNet, were trained using a dataset of 2,190 rice leaf images divided into five classes. The research process included data collection, preprocessing, model training, and performance evaluation using a confusion matrix. The results show that the VGG16 model achieved an accuracy of 98%, while MobileNet reached 95% accuracy. Thus, this method can serve as a modern solution for identifying rice plant diseases, supporting early detection efforts and enhancing agricultural productivity.