Title: Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

Abstract: In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. 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 NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

Keywords: Classification; wheat grains; image processing technique; neuro-fuzzy system (NFS)

Başlık: Görüntü İşleme Tekniklerine Dayalı Sinirsel-Bulanık Sistem Modeli Kullanarak Buğday Danelerinin Sınıflandırılması

Özet: Bu çalışmada, görüntü işleme tekniklerine (GİT) dayalı Sugeno tip sinirsel-bulanık sistem (SBS) modeli kullanarak buğday danelerinin, ekmeklik ve makarnalık olarak sınıflandırılması işlemi sunuldu. SBS modelinin eğitim ve test sürecinde kullanılacak veri setini elde etmek için 200 buğday danesinin görüntüleri yüksek çözünürlüklü kamera ile alınmıştır. Uzunluk, genişlik, alan, çevre ve doluluk olmak üzere 5 adet boyut özelliği, GIT kullanılarak elde edilmiştir. Sonra, SBS modeli, 180 buğday danesinin boyut özelliği giriş olacak şekilde kullanılarak eğitilmiş ve modelin doğruluğu da geriye kalan 20 buğday danesi ile test edilmiştir. Önerilen SBS modeli, test sürecinde sayısal olarak 0.0312 ortalama mutlak hata (OMH) ve %100'lük bir doğruluk ile buğday danelerini ekmeklik ve makarnalık olarak sınıflandırmıştır. Bu sonuçlar GİT ve SBS modelinin buğday danelerinin ekmeklik ve makarnalık olarak sınıflandırılmasında başarılı bir şekilde kullanılabileceğini göstermektedir.

Anahtar kelimeler: Sınıflandırma; buğday daneleri; görüntü işleme teknikleri; sinirsel-bulanık sistem

• Mollazade K, Omid M, Arefi A. Comparing data mining classifiers for grading raisins based on visual features. Computers and Electronics in Agriculture 2012; 84: 124–131.
• Sungur C, Ozkan H. A real time quality control application for animal production by image processing. Journal of the Science of Food and Agriculture 2015; 95: 2850-2857.
• Yu X, Liu K, Wu D, He Y. Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food and Bioprocess Technology 2012; 5: 1552–1563.
• Hu BG, Gosine RG, Cao LX, De Silva CW. Application of a fuzzy classification technique in computer grading of fish products. IEEE Transactions on Fuzzy Systems 1998; 6: 144–152.
• Al Ohali Y. Computer vision based date fruit grading system: Design and implementation. Journal of King Saud UniversityComputer and Information Sciences 2001; 23: 29–36.
• Galvez RP, Carpio FJE, Guadix EM, Guadix A. Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation. Journal of the Science of Food and Agriculture 2016; 96: 207–214.
• Petka J, Mocak J, Farkas P, Balla B, Kovac M. Classification of Slovak varietal white wines by volatile compounds. Journal of the Science of Food and Agriculture 2001; 81(15): 1533–1539.
• Berman M, Connor PM, Whitbourn LB, Coward DA, Osborne BG, Southan MD. Classification of sound and stained wheat grains using visible and near infrared hyperspectral image analysis. Journal of Near Infrared Spectroscopy 2007; 15(6): 351–358.
• Jamuna KS, Karpagavalli S, Revathi P, Gokilavani S, Madhiya E. Classification of seed cotton yield based on the growth stages of cotton crop using machine learning techniques. In International Conference on Advances in Computer Engineering, June 20-21, 2010, Bangalore, Karnataka, India, 2010, pp. 312–315.
• Guevara-Hernandez F, Gomez-Gil J. A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research 2011; 9(3): 672–680.
• Zapotoczny P. Discrimination of wheat grain varieties using image analysis: Morphological features. European Food Research and Technology 2011; 233: 769–779.
• Di Anibal CV, Ruisanchez I, Fernandez M, Forteza R, Cerda V, Callao MP. Standardization of UV–visible data in a food adulteration classification problem. Food Chemistry 2012; 134(4): 2326–2331.
• Prakash JS, Vignesh KA, Ashok C, Adithyan R. Multi class support vector machines classifier for machine vision application. In Machine Vision and Image Processing (MVIP), December 14-15, 2012, Taipei, Taiwan, pp. 197-199.
• Pazoki AR, Farokhi F, Pazoki Z. Classification of rice grain varieties using two artificial neural networks (MLP and NeuroFuzzy). Journal of Animal and Plant Sciences 2014; 24: 336–343.
• Muniz-Valencia R, Jurado JM, Ceballos-Magana SG, Alcazar A, Hernandez-Diaz J. Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks. Journal of Food Composition and Analysis 2014; 34(1): 7–11.
• De Oliveira EM, Leme DS, Barbosa BHG, Rodarte MP, Pereira RGFA. A computer vision system for coffee beans classification based on computational intelligence techniques. Journal of Food Engineering 2015; 171(1): 22–27.
• Shleeg AA, Ellabib IM. Comparison of mamdani and sugeno fuzzy interference systems for the breast cancer risk. International Journal of Computer, Information Science and Engineering 2013; 7(10): 387-391.
• Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979; 9(1): 62–66.
• Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 1993; 23(3): 665–685.