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.