An Efficient Segmentation Approach with Texture Analysis for Plant Disease Identification in CIELAB Space

Abstract

Agriculture is the major field and it plays a vital role in Indian economy. Day by day there is a rapid growth in Indian population hence it is very important that we need to enhance the crops production, but the effect of disease also more in this field which leads to the degradation in production of crops. Generally the diseases are Bacteria, Fungi, Viruses and Pests. Therefore, we must analyze the affected disease in prior to enhance the production. Here in this, we had implemented an efficient grouping of similar elements (GSE) approach with the texture analysis in CIELAB color space. Our proposed frame work has been designed in five modules. These modules help us to find the disease affected leaf area and the type of disease with the support of a classifier known as Support Vector Machine (SVM). Simulation experiments show that the proposed approach performed superior to the conventional algorithms.
Keywords: Agriculture, crops field, disease detection and identification, segmentation, clustering, feature extraction, GSE, statistical analysis and Support vector machine