Multi Class Support Vector Machine Based Plant Leaf Disease Detection from Color Texture And Shape Pictures

Abstract

Agriculture plays a vital role in the economy of many countries, and crop productivity is highly dependent on plant health.Plant diseases can significantly reduce crop yield and quality if not detected at an early stage.Traditional disease identification methods rely on manual inspection by agricultural experts, which can be time-consuming, expensive, and sometimes inaccurate.Recent advancements in image processing and machine learning have enabled automated systems for plant disease detection.This research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images.The proposed approach captures leaf images, performs preprocessing to remove noise and enhance image quality, segments the infected region, and extracts relevant features.These features are then used to train a Multi-Class SVM model capable of classifying different plant diseases.The combination of color, texture, and shape characteristics improves classification accuracy by providing comprehensive information about disease symptoms present on the leaf surface.Experimental analysis demonstrates that the proposed method can effectively identify multiple plant diseases with high accuracy while reducing the dependency on manual diagnosis.The developed system offers a cost-effective and efficient solution for farmers and agricultural professionals, helping in early disease detection and timely treatment recommendations.The proposed approach contributes to the advancement of smart agriculture and precision farming technologies.

Code and data availability

The paper describes a Multi-Class SVM leaf disease detection pipeline but provides no dataset name, deposit, code availability statement, or any public URL. No paper-specific public assets are identifiable from the supplied blocks.

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