Vietnam is a country that has the advantage in agriculture, especially rice. Rice is one of the primary food grains which provides sustenance to almost fifty percent of the world population and promotes a huge amount of employment. Hence, detection and prevention of diseases on rice are very important to help enhance rice production. This work proposes an approach for rice leaf disease detection on mobile devices using deep learning technique. Specifically, it proposes using EfficientNet which is a variant in deep learning networks for classification. This approach also utilizes the pre-trained model on imageNet for transfer learning. The proposed model can detect five types of images including three common types of diseases on rice leaf (e.g., brown spot, hispa, and leaf blast), healthy rice leaf, and other leaves. The model was trained on 1790 images and produced 95% validation accuracy. Finally, this model was converted to tflite format for running on mobiles or IoT devices. An Android application was built for rice leaf disease detection using the proposed model. When applying in practice on a mobile device, it took about 1.7 s for detecting and providing a treatment solution for a disease, thus this could be a useful solution to help the farmers.