WHEAT DISEASE IDENTIFICATION SYSTEM USING IMAGE SEGMENTATION AND THRESHOLD FILTER

Abstract

Abstract: - The rapid growth of digital imaging made image processing techniques widespread over various applications such as medical, industrial, engineering and real life problem. Image enhancement, segmentation, feature extraction, classification and identification play an essential role in image processing applications. Keeping this as motivation, this research work proposes wheat image segmentation and feature extraction techniques using computational intelligence and image processing techniques. The implemented scheme starts with image segmentation using morphological set to extract the zones of interest and then to enhance the edges surrounding it. Further, feature extraction using threshold filter to gray level co-occurrence matrix is presented. Further, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. In this work, we develop user-friendly wheat disease reference architecture to provide on-field disease detection and prediction using cloud analytics. Keywords: - Image Processing, Wheat Disease, Image Segmentation, Morphological Operation