Prediction of rice disease from leaves using deep convolution neural network towards a digital agricultural system

Abstract

Rice is considered as the main food for about 140 million people in Bangladesh. Rice, as a food, does not only fulfill the protein or calorie intake of an average person, but also rice production plays a vital role in terms of rural employment and GDP of the country. However, the production of rice is hampered because of many diseases of rice leaves. The objective of this work is to develop a model which can predict those diseases so that farmers can take appropriate action. This work presents a CNN based model which provides 97.40% accurate results in predicting various diseases of rice leaves. Using a dataset of over 900 images of diseases and healthy leaves and following the technique of 10-fold cross validation, the model was trained to identify 4 common rice diseases. This is the highest accuracy gained for only rice disease prediction to the best of our understanding with such a large dataset covering at least 4 diseases. The results of the simulation represent the feasibility and efficacy of the proposed model.