Leaf Disease Detection and Severity using Support Vector Machine

This paper presents a Leaf Disease Detection and Severity Analysis System designed to improve crop health monitoring through intelligent image processing and automated classification techniques. The system analyses leaf images in real time and identifies disease patterns using machine learning methods such as Support Vector Machine (SVM). Based on this analysis, leaves are classified into healthy and diseased categories, and specific disease types are detected using extracted features like color, texture, and shape. A threshold-based decision mechanism is applied to estimate disease severity by calculating the proportion of affected leaf area and categorizing the infection level accordingly. Unlike traditional agricultural practices that rely on manual inspection, the proposed system ensures consistent and accurate detection through automation. The system is implemented using a user-friendly interface integrated with backend processing modules and locally executed machine learning models to ensure efficiency and reliability. Experimental results demonstrate a significant improvement in disease detection accuracy and effective severity estimation. The proposed approach provides a scalable and adaptive solution for enhancing crop management among farmers and agricultural professionals