Determination of Classification Parameters of Barley Seeds Mixed with Wheat Seeds by using ANN
Kadir Sabanci, Cevat Aydin Department of Electrical and Electronics Engineering, Batman University, Batman, Turkey & Department of Agricultural Machinery, Selçuk University, Konya, Turkey
Abstract: One of the basic problems that cause loss of yield in wheat is weed seeds that mixed with wheat seeds. In this
study, discrimination of barley seed which mixed with wheat seeds has been realized. Classification of wheat and barley seeds
has been achieved by using artificial neural network and image processing techniques. In the study, image processing
techniques and the use of artificial neural network have been made possible with Matlab software. By using Otsu method,
histogram data of seed images that were taken from web camera was obtained. By using histogram data, with multi-layered
artificial neural network model, the system was educated and classification was made. Besides, wheat and barley seeds in the
picture info where mixed seeds taken from the web camera exist were counted.
Keywords: Artificial Neural Networks, Systems Security, Seed Images Determination of Classification Parameters of Barley Seeds Mixed with Wheat Seeds by using ANN
References: Yaman, K. (2000). Görüntü i_leme yönteminin Ankara h1zl1 rayl1 ula_1m sistemi güzergah1nda sefer araliklarinin
optimizasyonuna yönelik olarak incelenmesi. Yay1nlanmam1_ Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü.
 Castelman, R. K. (1996). Digital image processing. Prentice hall, Englewood Cliffs, New Jersey, USA. Neuman, M. R., H. D.
Sapirstein, E. Shwedyk and W. Bushuk. 1989. Wheat grain colour analysis by digital image processing. II. Wheat class
discrimination. Journal of Cereal Science 10. 183-188.
 Keefe, P. D. (1992). A Dedicated wheat grain image analyzer. Plant Varieties and Seeds 5. 27-33.
 Trooien, T. P., Heermann, D. F. (1992). Measurement and simulation of potato leaf area using image processing. Model
development. Transactions of the ASAE, 35 (5) 1709-1712.
 Pérez, A. J., Lopez, F., Benlloch, J. V., Christensen, S. (2000). Colour and shape analysis techniques for weed detection in
cereal fields. Computers and Electronics in Agriculture, 25. 197-212.
 Dalen, G. V. (2004). Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and
image analysis. Food Research International, 37. 51-58.
 Jayas, D. S., Karunakaran, C. (2005). Machine vision system in postharvest technology. Stewart Postharvest Review, 22.
 Fausett, L. (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall.
 Kavas, G., Kavas, N. (2012). G1dalarda yapay sinir alar1 ve bulan1k mant1k. DÜNYA yay1nc1l1k, GIDA Dergisi 2012-01. 93-
 Yaman, K., Sarucan, A., Atak, M., Aktürk, N. (2001). Dinamik çizelgeleme için görüntü i_leme ve ARIMA modelleri yard1m1yla
veri haz1rlama. Gazi Üniv. Müh. Mim. Fak. Dergisi, 16 (1) 19-40.
 Öztemel, E. (2003). Yapay Sinir Alari. Istanbul: Papatya Yay1ncilik