Rice Leaf blight Disease detection using multi-classification deep learning model

One of the main crops in agricultural fields is considered to be rice, which is produced and used extensively over the world. Due to this very reason, there is always a high risk of rice crops getting prone to disease. Rice leaf blight (RLB) disease detection system has been developed using 3000 real-time image dataset which is trained and validated on algorithm on deep learning (DL) convolutional neural networks (CNN) approach. The whole experiment is conducted in two phases, first is the binary classification of fine and RLB infected crop has been experimented, and secondly, multi-classification of RLB disease crop based on RLB disease severity is conducted. The accuracy of 94.33 % and 95.3 % have been achieved in binary and multi-classification infection severity. In addition to this the proposed study will contribute in enhancement of quality of life, technological progress, research development and industry innovation.