Sistem Deteksi dan Monitoring Terpusat Penyakit Daun Padi Berbasis Mobile Menggunakan MobileNetV2

Abstract

Rice leaf diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut are a major threat to agricultural productivity in Indonesia because manual identification by farmers remains subjective, slow, and error prone. This study aims to develop a rice leaf disease detection system that integrates a Flutter based mobile application with a web based monitoring dashboard, so that diagnosis can be performed independently by farmers while being monitored centrally by system administrators. The method applied is a Convolutional Neural Network with a transfer learning approach on the MobileNetV2 architecture, trained using an expanded dataset of 300 rice leaf images obtained from primary field observation data in Klaten and secondary data from the Kaggle platform, then converted to TensorFlow Lite format to enable on-device inference on mobile devices. The system backend was built using Flask and a MySQL database to manage user data and detection history. Experiments on four hyperparameter scenarios showed that a configuration of 20 epochs, batch size 32, and learning rate 0.001 produced the best performance with a test accuracy of 95.24 percent and a loss of 0.1506 without significant indication of overfitting. The final model size of 9.71 MB with an average inference time of under 2 seconds proves that this system is feasible to be applied as an accurate, lightweight, and responsive rice disease diagnosis tool in the field.