Detection and Prediction of Rice Leaf Disease Using a Hybrid CNN-SVM Model

Abstract

Agriculture is one of India’s greatest money makers and a measure of financial growth. Rice is one of India’s most widely grown crops as a staple diet. Rice crops have been shown to be heavily afflicted by illnesses, resulting in significant losses in agriculture. Rice leaf diseases not only cause a loss of revenue for farmers, but they also decrease the quality of their final output. External appearances of diseased rice leaves can be subjected to image processing processes. On the other hand, disease sickness may vary depending on the different leaves. Each disease has its own distinct features, some of leaves have the same colour but various shapes, while others have different colours but the same shapes. Farmers are sometimes confused and unable to make an accurate judgement when it comes to pesticide choosing. To solve this problem, a hybrid CNN (Inception-ResNet)-SVM model for detecting and treating damaged rice leaves has been developed. In this designed model, the images are collected and gathered by capturing rice leaf using camera at the agricultural field. These images are refined to improve image quality and visibility for reliable estimation, and then segregated using Grab-Cut algorithm to eliminate undesired sections of image. Features of the segmented images are extracted and classified using hybrid CNN (Inception-Resnet V2)-SVM algorithm. The developed model’s study results are analysed and discussed to recent techniques. The suggested model achieved accuracy, precision, recall, and error values of 0.97, 0.93 and 0.03 accordingly. As a conclusion, suggested model outperforms revious methodologies.