Plant Disease Classification Using Image Segmentation and SVM Techniques

Abstract

This work presents a technique for recognizing plant leaf illness and a methodology for cautious discovery of sicknesses. The objective of proposed work is to analyze the illness of leaf utilizing picture preparing and (SVM) Support Vector Machine. The infections on the leaf are basic issue which makes the sharp diminishing in the creation of leaf. The investigation of premium is the leaf as opposed to entire Maize leaf  plant in light of the fact that around 85-95 % of infections happened on the leaf like, the procedure to distinguish leaf sickness in this work incorporates K mean's grouping calculation for division and Support Vector Machine.

The goal of this work is to actualize picture investigation and characterization strategies for location of leaf sicknesses and grouping. The proposed system comprises of four sections. They are Image preprocessing, Segmentation of the leaf utilizing K-implies grouping to decide the ailing zones, highlight extraction and Classification of infections. Surface highlights are extricated utilizing factual Gray-Level Co-Occurrence Matrix (GLCM) highlights and characterization is finished utilizing Support Vector Machine (SVM).). .