Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 2349-8161
Online ISSN: 2349-817X

  About ISEJ
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Paper Submission
Contact us
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)


Information Security Education Journal (ISEJ)

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
Full_Text   PDF 242 KB   Download:   122  times
References:[1] 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. [2] 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. [3] Keefe, P. D. (1992). A Dedicated wheat grain image analyzer. Plant Varieties and Seeds 5. 27-33. [4] 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. [5] 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. [6] 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. [7] Jayas, D. S., Karunakaran, C. (2005). Machine vision system in postharvest technology. Stewart Postharvest Review, 22. [8] Fausett, L. (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall. [9] Kavas, G., Kavas, N. (2012). G1dalarda yapay sinir alar1 ve bulan1k mant1k. DÜNYA yay1nc1l1k, GIDA Dergisi 2012-01. 93- 96. [10] 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. [11] Öztemel, E. (2003). Yapay Sinir Alari. Istanbul: Papatya Yay1ncilik

Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|


Copyright 2013 socio.org.uk