Automatic Identification of Leaf Disease using Image Segmentation Method

Abstract

An Internet of Things (IOT) based plant diseased leaf segmentation and recognition method is proposed based on Fusion of Super-pixel clustering, K-mean clustering and Grey Level Cooccurrence Matrix (GLCM) algorithms. Firstly, the color diseased leaf image is divided into a few compact super-pixels by super-pixel clustering algorithm. Then K means clustering algorithm is employed to segment the lesion image from each super-pixel. Finally, the GLCM features are extracted from three color components of each segmented lesion image and its gray scale image and the feature points are stored in a database. The experiment results on four plant diseased leaf image databases indicate that the proposed method is effective. This paper provides a feasible solution to anybody for automatic identification the leaf diseases at initial stages by using leaf image segmentation and plant disease recognition.