Plant Disease Detection Using Fuzzy Classification

Abstract

            The economic growth of a nation mainly depends on agricultural productivity. This is the one of the reasons that early prediction of plant diseases plays a crucial role in agricultural field, as acquiring disease in plants is quite natural. Proper care has to be taken in this area to prevent serious effects on plants in earlier stages by which respective product quality, quantity and/or productivity can be improved.   Automatic disease detection techniques are highly valuable for reducing the tedious work of monitoring in big farms of crops by detecting disease symptoms appearing on the plant leaves in a very earlier stage. Therefore, a new algorithm is proposed for automatic detection and classification of plant leaf diseases using fuzzy classification technique. Image segmentation forms an important aspect for disease detection in plant leaf disease, which is done by using K-means algorithm. Using the Fuzzy membership function, structure relationships between vertices are viewed in the terms of degree for detecting the plant disease. A test image is compared with database image and then dissimilarity is calculated with extracted parameters like skewness, extract mean and deviation. The accuracy of 93% is achieved by the proposed system, which is more as compared with that of the existing system.