Utilizing Multi-Class Support Vector Machine to Detect Plant Leaf Diseases

Abstract

Abstract: Plant disease has a large influence on agricultural output and food security. For successful illness management, early detection and correct diagnosis of these disorders are critical. Digital image processing techniques have emerged as a viable tool for automated plant leaf disease identification in recent years. The goal of this study is to use image processing to create a more efficient and accurate system for detecting leaf illness. K-means clustering, Gray-Level Co-Occurrence Matrix (GLCM), and Support Vector Machine are used to create the model. Kmeans clustering is employed for image segmentation, GLCM, and multiclass SVM for feature extraction and illness classification respectively. The maximum accuracy is 98.4%