Enhancing Leaf Disease Detection with SVM: A Reliable Approach

Enhancing Leaf Disease Detection with SVM: A Reliable Approach

J. K. Kokate1, Dr. Sunil Kumar2, Dr. Anant G, Kulkarni3

1* Research Scholar, Electronics and Communication Department, Kalinga University, Raipur, India

2 Professor Electrical & Electronics Department Kalinga University, Raipur, India

3 Principal, Electronics and Telecommunication Department, Siddhivinayak Technical Campus, Shegaon, India

Abstract: - Detecting leaf diseases with high accuracy is a topic of utmost significance and interest. Support Vector Machines (SVM), a powerful machine learning algorithm known for its effectiveness in classification tasks, can be employed to distinguish between healthy and diseased leaves. Leaf diseases in crops pose a major threat to global food security, leading to substantial economic losses and reduced crop yields. Timely and precise identification of these diseases is crucial for effective disease management and prevention. However, manual identification methods are prone to inaccuracies and time-consuming processes.

In this study, we investigate the efficacy of SVM classifiers for identifying leaf diseases. Our Support Vector Machine (SVM) model achieves an impressive accuracy rate of 99.84% in accurately identifying leaf diseases. These findings have significant implications for the practical implementation of SVM classifiers, as they demonstrate the ability to accurately recognize various plant ailments and enhance crop disease control, ultimately improving crop yields.

Keywords: Accurate, Comparison, Classifiers, Hybrid Deep Learning, plant growth