Rice Leaf Diseases Classification Using Deep Learning Techniques

Abstract

Rice is the primary food source for a significant portion of the global population and the productivity of rice crops can be severely impacted by diseases. These diseases can cause significant yield loss, which can have a major impact on food security. Accurate and timely detection of rice leaf diseases is therefore crucial for implementing effective control measures to minimize yield loss. This study aims to work towards the detection of rice leaf diseases, specifically leaf smut, brown spot, and bacterial leaf blight, using a deep learning approach. ResNet50 with an added NN architecture was trained on a dataset consisting of images of rice leaves collected from the Bahribahri rice farm in Indonesia. The dataset includes 4000 photos of each of the three diseases listed above in addition to an equal number of photographs of rice crops in good health. The dataset is used to train the model so that it can identify the presence of the diseases in new images. The results show that the use of ResNet50+NN achieved an accuracy of 99.5% in detecting the three diseases, making it a promising tool for rice leaf disease detection in a farm setting. In summary, this study provides an efficient and accurate solution for rice leaf disease detection, which is critical for maintaining rice productivity and food security.