A Combined HSV and GLCM Approach for Paddy Variety Identification from Crop Images

Abstract

Paddy is the staple food of India and many other countries. It is very essential to find out the best variety that promises good yield. This paper presents a methodology to identify variety of paddy field images. In this work, we have considered 22 varieties of paddy field images and they are divided into three classes based on physical features as light green, lush green and pale green. Identification is done using color, texture and combination of both types of features. Color features are extracted using HSV and texture using GLCM. Artificial Neural network (ANN) is used for identification of variety of paddy field images. Considering only color and texture, the results were not satisfactory. Combined features resulted in n accuracy of 85.7% in light green, 83.1% in lush green and 100% in pale green class. The work is an attempt to disseminate knowledge about new variety of paddy crop required to promote the large scale cultivation.