Plant Disease Identification using Segmentation Techniques

Abstract: Plant diseases are the major cause of production in agricultural and medicinal industry worldwide. It leads to economic losses. Monitoring of health and detection of diseases in plants and trees is a critical issue. This thesis describes a method for identification of diseased rose plants based on some important features extracted from its leaf images. The most significant part of research on plant disease to identify the disease based on CBIR that is mainly concerned with the accurate detection of disease of rose plant. We present an approach where the leaf is identified based on its leaf features such as Color, shape using Color histogram and edge histogram. The combination of CBIR, Canny edge detector and HSI Color model identifies the disease accurately.

Keywords: Segmentation, Edge detection, canny algorithm, Edge histogram, HSI histogram, SVM classifier.