LEAF DISEASES DETECTION USING SUPPORT VECTOR MACHINE

Abstract

Identification of the leaf diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the leaf diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on leaf is very critical for sustainable agriculture. It is very difficult to monitor the leaf diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing time. Hence, image processing is use for the detection of leaf diseases. Disease detection involves the steps like image acquisition, image pre- processing, image segmentation, feature extraction and classification. In this paper we present an automatic detection of leaf diseases using image processing techniques. The presented system is a software solution for automatic detection and computation of texture statistics for plant leaf diseases. The processing system consists of four main steps, first a color transformation structure for the input RGB image is created, then the green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. From the texture statistics, the diseases, if present on the plant leaf are evaluated.