Diseased Plant Leaf Prediction using Image Processing and Multi-SVM

Abstract

Agriculture plays a pivotal role in determining a nation's prosperity, particularly in a country like India where over 68% of the population relies on it for their livelihoods. Crop infections pose a significant threat, not only to the livelihoods of farmers but also to the overall economic stability of the nation. Effective measures must be taken to diagnose and treat plant diseases promptly in order to mitigate substantial losses and safeguard crop yields. In this study, we propose a novel approach combining image processing and machine learning techniques to identify and classify plant leaf diseases. Our method utilizes a multi-class Support Vector Machine (SVM) classifier, incorporating texture analysis using Gray-Level Co-occurrence Matrix (GLCM) features. The results of our predictive model exhibit promising outcomes, achieving an impressive accuracy rate exceeding 90% during the simulation phase, which holds great potential for practical implementation in the agricultural sector.