Rice Diseases Recognition Using Effective Deep Learning Models

Abstract

Rice is the most important grain in Thailand for both consuming and exporting. One of the critical problems in rice cultivation is rice diseases, which affects directly to the yield. Disease recognition by a human is hard and the performance depends on the farmer's experience. To overcome this problem, we did two folds of contributions. First, an infield rice disease image dataset was created. Second, a number of deep learning models including ResNets and DenseNets were applied to classify such rice diseases. The experimental results reveal that the proposed framework can achieve high accuracy, more than 95% in average, and has potential to be implemented and provide to Thai farmers in the future.