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

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Author(s)

Archana A. Chaugule 1,* Suresh N. Mali 1

1. DYPIET/Computer Department, Pune, 411017, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2014.12.05

Received: 24 Jul. 2014 / Revised: 30 Aug. 2014 / Accepted: 2 Oct. 2014 / Published: 8 Nov. 2014

Index Terms

Color, Mean, Moments, Standard deviation, Variance, Shape features

Abstract

This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them 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 set from the features was identified for accurate classification. The Shape-n-Color feature set outperformed in almost all the instances of classification.

Cite This Paper

Archana A. Chaugule, Suresh N. Mali,"Evaluation of Shape and Color Features for Classification of Four Paddy Varieties", IJIGSP, vol.6, no.12, pp.32-38, 2014. DOI: 10.5815/ijigsp.2014.12.05

Reference

[1]Archana Chaugule and Dr. Suresh Mali, “Seed Technological Development – A Survey,” ACEEE, Proc. of International Conference on Information Technology in Signal and Image Processing, doi:03.LSCS.2013.6.528, pp:71-78, 2013.

[2]Xiao Chena, Yi Xunb, Wei Li, Junxiong Zhang, “Combining Discriminant Analysis and Neural Networks for Corn Variety Identification,” Elsevier, Computers and Electronics in Agriculture 71S, pp: S48-S53, 2010.

[3]Min zhao, Wenfu Wu, Ya qiu Zhang and Xing Li, “Combining genetic algorithm and SVM for corn variety identification,” 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, Jilin, China, 978-1-61284-722-1/11/$26.00 ?2011 IEEE, pp:990-993, August 19-22, 2011.

[4]S.P. Shouche, R. Rastogi, S.G. Bhagwat, Jayashree Krishna Sainis, “Shape analysis of grains of Indian wheat varieties,” Elsevier, Computers and Electronics in Agriculture 33, pp: 55–76, 2001.

[5]Chandra B. Singh, Digvir S. Jayas, Jitendra Paliwal, Noel D.G. White, “ Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging,” Elsevier, Computers and Electronics in Agriculture, 73, pp: 118–125, 2010.

[6]Marian Wiwart, Elzbieta Suchowilska, Waldemar Lajszner, ukasz Graban, “Identification of Hybrids of Spelt and Wheat and their Parental Forms Using Shape and Color Descriptors,” Elsevier, Computers and Electronics in Agriculture, 83, pp: 68-76, 2012.

[7]Kuo-Yi Huang, “Detection and Classification of Areca Nuts with Machine Vision,” Elsevier, Computers and Mathematics with Applications, 64, pp: 739-746, 2012.

[8]Li Jingbin, Chen Bingqi, ,Shao Luhao,Tian Xushun, Kan Za, “Variety Identification of Delinted Cottonseeds Based on BP Neural Network,” Transactions of the Chinese society of Agricultural Engineering, Vol. 28, pp: 265-269, Oct.2012.

[9]H.K. Mebatsion, J. Paliwal, D.S. Jayas , “Automatic classification of non-touching cereal grains in digital images using limited morphological and color features,” Elsevier, Computers and Electronics in Agriculture 90, pp: 99–105, 2013.

[10]Kohei Arai, Indra Nugraha Abdullah, Hiroshi Okumura, “ Image Retrieval Based on Color, Shape, and Texture for Ornamental Leaf with Medicinal Functionality Images,” I.J. Image, Graphics and Signal Processing, 2014, 7, 10-18 Published Online June 2014 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2014.07.02.

[11]Symons, S.J., Fulcher, R.G., “Determination of wheat kernel morphological variation by digital image analysis, I Variation in eastern Canadian milling quality wheats,” J. Cereal Sci. 8, pp: 211–218, 1988.

[12]Hu M K, “Visual pattern recognition by moment invariant,” IRE Transactions of Information Theory, 8, pp: 179–187, 1962.

[13]Gonzalez R C; Woods R E; Steven L. Eddins, “Digital Image Processing Using MATLAB,” Second Edition, Tata McGraw Hill Pvt. Ltd., New Delhi, 2010.

[14]Jain A K, Moment representation. In: Fundamentals of Digital Image Processing (Jain A K, ed), Prentice Hall of India Private Limited, New Delhi, 1995. 

[15]Mark S. Nixon, Alberto S. Aguado, Newnes, “Feature Extraction and Image Processing,” Oxford Auckland Boston Johannesburg Melbourne New Delhi, 2002.