Rice Disease Detection Based on Image Processing Technique

Abstract

Rice plant diseases are major problems in Bangladesh. Detection and monitoring of these rice plant diseases is a critical issue. Rice plants are affected in various kind of disease like hispa, brown spot, and leaf blast and show the syndrome in the leaf of these diseases. If these diseases are detected early and take appropriate action, it will restrain extensive economic loss for the farmer. In this research paper, the proposed model will successfully classify and find out the rice leaf diseases based on image processing techniques. Machine learning algorithm CNN is used to implement this model. Healthy and disease-affected leaves are taken for the proposed method and separated healthy and unhealthy characteristics of rice plant leaves. After that, these images are being processed with the proposed model and classified the leaf as either infected by disease or healthy. This proposed model provided the accuracy of 90%. This model successfully identifies the infected and healthy rice plant.