Analysis of Deep Learning Implementation Using Xception for Rice Leaf Disease Classification - Puspitaningrum, Niken; Rahardi, Majid (Journal of Applied Informatics and Computing, 2026)
Identifying rice leaf diseases plays a crucial role in maintaining agricultural productivity and preventing massive losses. In recent years, deep learning models have shown very promising performance in plant disease classification tasks. This study proposes a Rice Leaf Disease Detection System based on the Xception model from Keras Applications, an architecture that is still relatively unexplored for rice plant disease cases. Through preprocessing, data augmentation, and model refinement, the developed system achieved a training accuracy of 93% and a testing accuracy of 89% in classifying rice leaf conditions. In addition, metric evaluation showed precision, recall, and F1-score values of 89%, reflecting the model's ability to make consistent and balanced predictions. The trained model was then integrated into a web-based application to facilitate real-time disease diagnosis through image uploads. The results of this study prove the effectiveness of the Xception architecture in extracting agricultural image features and its potential for application in artificial intelligence-based smart farming systems.