ANN-BASED CLASSIFIER TRAINED BY BAYESIAN REGULARIZATION FOR WHEAT GRAINS THROUGH COLOUR FEATURE

Yıl 2017, Cilt: 1 Sayı: 1, 46 - 53, 27.12.2017

Berat Yıldız , Abdurrahim Toktaş , Enes Yiğit , Ahmet Kayabaşı , Kadir Sabancı , Mustafa Tekbaş

Öz

In this paper, a color feature-based classification of
the wheat grains into bread and durum using artificial neural network (ANN)
model with bayesian regularization (BR) learning algorithm is presented. Images
of 200 wheat grains are taken by a high resolution camera in order to generate
the data set for training and testing processes of the ANN-BR model. Data of 3
main colour features (R, G and B) for 200 wheat grains (100 for durum and 100
for bread) are acquired for each grain using image processing techniques
(IPTs). Features of R, G and B are separately determined by taking arithmetic
average of the pixels within each grain. Several colour features of R/TRGB,
G/TRGB, B/TRGB, R-G, G-B and R-B where TRGB is the total of R+G+B are
reproduced. Then ANN-BR model input with the 9 colour parameters are trained through
180 wheat grain data and their accuracies are tested via 20 data. The ANN-BR
model numerically calculate the outputs with mean absolute error (MAE) of
0.0060 and classify the grains with accuracy of 100% for the testing process.
These results show that the ANN-BR model can be successfully applied to
classification of wheat grains.

Anahtar Kelimeler

Classification, wheat grains, image processing technique, artificial neural network, bayesian regularization learning algorithm

Kaynakça

• 1) K. Mollazade, M. Omid and A. Arefi, Comparing data mining classifiers for grading raisins based on visual features 84. Comput Electron Agr:124–131 (2012).
• 2) C. Sungur and H. Ozkan, 2015. A real time quality control application for animal production by image processing 95. Journal of the Science of Food and Agriculture. 95 2850–2857.
• 3) X. Yu, K. Liu, D. Wu and Y. He, Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food Bioprocess Tech 5:1552–1563 (2012).
• 4) B.G. Hu, R.G. Gosine, L.X. Cao and de C.W. Silva, Application of a fuzzy classification technique in computer grading of fish products. IEEE T Fuzzy Syst 6:144–152 (1998).
• 5) Y. Al Ohali, 2011. Computer vision based date fruit grading system: Design and implementation 23. Journal of King Saud University-Computer and Information Sciences. 23 29–36.
• 6) R.P. Gálvez, F.J.E. Carpio, E.M. Guadix and A. Guadix, 2016. Artificial neural networks to model the production of blood protein hydrolysates for plant fertilization 96. Journal of the Science of Food and Agriculture. 96 207–214.
• 7) J. Pet'ka, J. Mocak, P. Farkaš, B. Balla and M. Kováč, 2001. Classification of Slovak varietal white wines by volatile compounds 81. Journal of the Science of Food and Agriculture. 81 1533–1539.
• 8) M. Berman, P.M. Connor, L.B. Whitbourn, D.A. Coward, B.G Osborne and M.D. Southan, 2007. Classification of sound and stained wheat grains using visible and near infrared hyperspectral image analysis 15. Journal of Near Infrared Spectroscopy. 15 351–358.
• 9) K.S. Jamuna, S Karpagavalli, P. Revathi, S. Gokilavani and E. Madhiya, Classification of Seed Cotton Yield Based on the Growth Stages of Cotton Crop Using Machine Learning Techniques. International Conference on Advances in Computer Engineering 20-21 June 2010; Bangalore, Karnataka, India, 312–315 (2010).
• 10) F. Guevara-Hernandez and J. Gomez-Gil, 2011. A machine vision system for classification of wheat and barley grain kernels 9. Spanish Journal of Agricultural Research. 9 672–680.
• 11) P. Zapotoczny, Discrimination of wheat grain varieties using image analysis: morphological features. Eur Food Res Technol 233:769–779 (2011).
• 12) C.V. Di Anibal, I. Ruisánchez, M. Fernández, R. Forteza, V. Cerdà and M.P. Callao, Standardization of UV–visible data in a food adulteration classification problem. Food Chem 134:2326–2331 (2012).
• 13) J.S. Prakash, K.A. Vignesh, C. Ashok and R. Adithyan, Multi class Support Vector Machines classifier for machine vision application. In Machine Vision and Image Processing (MVIP) 14-15 Dec. 2012; Taipei, Taiwan, 197–199 (2012).
• 14) A.R. Pazoki, F. Farokhi and Z. Pazoki, 2014. Classification of rice grain varieties using two Artificial Neural Networks (MLP and Neuro-Fuzzy) 24. Journal of Animal and Plant Sciences. 24 336–343.
• 15) R. Muñiz-Valencia, J.M. Jurado, S.G. Ceballos-Magaña, Á. Alcázar and J. Hernández-Díaz, 2014. Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks 34. Journal of Food Composition and Analysis. 34 7–11.
• 16) E.M. De Oliveira, D.S. Leme, B.H.G. Barbosa, M.P. Rodarte and R.G.F.A. Pereira, 2016. A computer vision system for coffee beans classification based on computational intelligence techniques 171. Journal of Food Engineering. 171 22–27.
• 17) M. Zandieh, A. Azadeh, B. Hadadi and M. Saberi, 2009. Application of neural networks for airline number of passenger estimation in time series state. Journal of Applied Science. vol. 9, no. 6, 1001–1013.
• 18) N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE T Syst Man Cyb 9: 62–66 (1979).