An Automatic Leaf Disease Detection System for Legume Species

Abstract

Legumes are crucial species which are used by the community worldwide. In this manuscript, a two stage approach to identify infected leaf region percentage in legumes (particularly Groundnut and Soybean) is proposed. First stage classifies between a healthy and a diseased leaf sample. Second stage detects the diseased region and identifies the proportion of leaf infected area. The two stage approach provides high accuracy and also, shows that texture features plays an important role for classification of healthy and diseased leaves. The experimental results obtained on a self-collected leaf image dataset show that the proposed approach accurately identifies the diseased region in legumes. The proposed methodology can also be used for the classification of different disease types.