Digital Leaf Image Disease Detection By Content Features And Linear Kernel

Authors: Udhav Kumar Mishra, Professor Sujeet Gautam, Professor S Vishwakarma

Abstract: Agriculture plays a fundamental role in human civilization by ensuring food security and providing essential resources. As plant diseases directly affect crop yield and quality, their early and accurate detection is critically important. The proposed model focuses on extracting Texture and Content features to represent color distribution in leaf images. These features are combined to form a robust representation of healthy and diseased leaf characteristics. A multi-class Support Vector Machine (SVM) with a linear kernel is employed for classification due to its simplicity, efficiency, and suitability for high-dimensional feature spaces. Experimental evaluation on a real tomato plant leaf dataset demonstrates that the proposed approach significantly improves classification accuracy and effectively distinguishes between multiple types of leaf diseases.