Abstract
In this article, classification of wheat varieties is aimed with the help of multiclass support vector machines (M-SVM) and binary particle swarm optimization (BPSO) algorithm. For each wheat kernel, 9 geometric and 3 color features are obtained from the digital images which are belong to 5 wheat type. Wheat types are classified using M-SVM. In order to increase the reliability of the classification process, 2 fold cross validation approach is implemented and this process repeated 250 times. Average classification accuracy is obtained as 91.5%. With the aim of increasing the classification accuracy and decreasing the process time, descriptive features are decreased by BPSO algorithm and reduced from 12 to 7. Average of classification accuracy is obtained as 92.02% using 7 features obtained from reduction with BPSO.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Du, C.-J., Sun, D.-W.: Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology 15, 230–249 (2004)
Berrueta, L.A., Alonso-Salces, R.M., Héberger, K.: Supervised pattern recognition in food analysis. Journal of Chromatography A 1158, 196–214 (2007)
Visen, N.S., Paliwall, J., Jayas, D.S., White, N.D.G.: Image analysis of bulk grain samples using neural Networks. Canadian Biosystems Engineering 46, 7.11–7.15 (2004)
Zapotoczny, P., Zielinska, M., Nita, Z.: Application of image analysis for the varietal classification of barley: Morphological features. Journal of Cereal Science 48, 104–110 (2008)
Choudhary, R., Paliwal, J., Jayas, D.S.: Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosystems Engineering 99, 330–337 (2008)
Tahir, A.R., Neethirajan, S., Jayas, D.S., Shahin, M.A., Symons, S.J., White, N.D.G.: Evaluation of the effect of moisture content on cereal grains by digital image analysis. Food Research International 40, 1140–1145 (2007)
Ramalingam, G., Neethirajan, S., Jayas, D.S., White, N.D.G.: Charecterization of the Influence of Moisture Content on Single Wheat Kernels Using Machine Vision. In: CSBE/SCGAB 2009 Annual Conference, Prince Edward Island, July 12-15 (2009), Paper No: CSBE09-708
Neethirajan, S., Karunakaran, C., Symons, S., Jayas, D.S.: Classification of vitreousness in durum wheat using soft X-rays and transmitted light images. Computers and Electronics in Agriculture 53, 71–78 (2006)
Wang, N., Zhang, N., Dowell, F., Pearson, T.: Determination fo durum vitreousness using transmissive and reflective images. In: 2003 ASAE Annual International Meeting, Las Vegas, Nevada, USA, July 27-30 (2003)
Utku, H., Köksel, H.: Use of Statistical Filters in the Classification of Wheats by Image Analysis. Joumat of Food Engineering 36, 385–394 (1998)
Shouche, S.P., Rastogi, R., Bhagwat, S.G., Sainis, J.K.: Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture 33, 55–76 (2001)
Dubey, B.P., Bhagwat, S.G., Shouche, S.P., Sainis, J.K.: Potential of Artificial Neural Networks in Varietal Identification using Morphometry of Wheat Grains. Biosystems Engineering 95(1), 61–67 (2006)
Raudys, S., Baykan, Ö.K., Babalık, A., Denisov, V., Bielskis, A.A.: Classifiers Fusion in Recognition of Wheat Viarieties. LNCS, vol. 447, pp. 62–71 (2007)
Babalık, A., Baykan, Ö.K., Botsalı, F.M.: Classification of Wheat Kernels Using Multi-Class Support Vector Machine. In: ISCSE 2010, International Symposium on Computing in Science & Engineering (2010) (article in press)
Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R.E.: Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture 71, 107–127 (2010)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Avşar, E., Kamaşak, M.E., Çataltepe, Z.: Tek-sınıf destek vector makineleri kullanılarak EEG işaretlerinin sınıflandırılması. In: BIYOMUT 2009, İzmir, Turkey, May 20-24 (2009)
Ekici, S., Yıldırım, S., Poyraz, M.: Mesafe korumak için bir örüntü tanıma uygulaması. Gazi Üniversitesi Mühendislik – Mimarlık Fakültesi Dergisi (24), 51–61 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948. Piscataway, NJ (1995)
Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of 1997 conference systems man cybernetics, pp. 4104–4108. Piscataway, NJ (1997)
Chang, C.C., Lin, C.J.: LIBSVM:a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hsu, C.W., Lin, C.J.: A Comparasion of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Babalık, A., Baykan, Ö.K., İşcan, H., Babaoğlu, İ., Fındık, O. (2010). Effects of Feature Selection Using Binary Particle Swarm Optimization on Wheat Variety Classification. In: Papasratorn, B., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds) Advances in Information Technology. IAIT 2010. Communications in Computer and Information Science, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16699-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-16699-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16698-3
Online ISBN: 978-3-642-16699-0
eBook Packages: Computer ScienceComputer Science (R0)