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Journal of Engineering
Volume 2014, Article ID 617263, 8 pages
http://dx.doi.org/10.1155/2014/617263
Research Article

Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties

DYPIET, Pimpri, Pune 411017, India

Received 19 May 2014; Accepted 30 July 2014; Published 18 August 2014

Academic Editor: Minoru Uehara

Copyright © 2014 Archana Chaugule and Suresh N. Mali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This research is aimed at evaluating the texture and shape features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of texture and shape features and neural network was done to classify four Paddy (rice) grains, namely, Karjat-6(K6), Ratnagiri-2(R2), Ratnagiri-4(R4), and Ratnagiri-24(R24). Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and used as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature from the features for accurate classification was identified. The shape feature set outperformed the texture feature set in almost all the instances of classification.