Smart Agriculture: Rice Leaf Disease Prediction and Classification using Transfer Learning and Deep Learning

Abstract

Agriculture is a key component in ensuring food security in the world and one of the most common staple foods in the world is rice. But there are a number of leaf diseases affecting rice, like bacterial leaf blight, brown spot, and rice blast, which cause lower production yield. In order to limit economic losses, it is necessary to be able to diagnose these diseases on time. This paper presents an intelligent prediction and classification of rice leaf disease prediction and classification using deep learning and transfer learning approaches. For that purpose, the proposed model uses two pre-trained transfer learning models, DenseNet201 and EfficientNetB3, and a conventional CNN, to classify healthy and diseased rice leaves from a set of image data. The performances of the three models are measured using the following performance measures: accuracy, precision, recall, and F1-score. From the experiments conducted, it can be noted that the best performing model among all three models is the EfficientNetB3, which shows an excellent classification accuracy of 95%. It performs better than both DenseNet201, whose accuracy is 92% and CNN model, whose accuracy is 83.5%. The reason behind the excellent performance of the EfficientNetB3 is that the architecture of the model is optimized in such a way that it is able to extract the most discriminative features with less computational complexity. The proposed framework can offer an efficient, reliable, and automated solution for the early detection of the diseases in the leaves of rice crops.