Detection of leaf diseases using color and shape models

Abstract

This paper handles with detection of leaf diseases using deep learning networks which have learned the color and shape parameters of leaf diseases. This paper considers the color distribution and shape information of leaf diseases, and exploits two deep leaning networks in training the normal leaves and diseases. The input color image is partitioned into small segments using color clustering, and the color information of each segment is inspected by the Color Network. When a segment is determined as abnormal (that is, disease segment), the shape parameters of the segment are inspected by Shape Network to classify the disease types. This paper uses HSV color space for Color Network and proposes 24 parameters for Shape Network such as boundary length ratio, densities of subregions, correlation coefficients of x-y coordinates in the disease segments. According to the experiments with three types of diseases (type A, B, C) for images of iceberg, strawberry, coffee, sunflower, chinar, blackgram, citrus, and apple leaves images, leaf diseases are detected with 97.9% recall for a segment unit and 99.3% recall for an input image where there are more than two disease segments.