Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2015, Cilt: 3 Sayı: 2, 86 - 89, 01.04.2015

Kadir Sabanci Murat Koklu Muhammed Fahri Unlersen

https://doi.org/10.18201/ijisae.74573
Cited By: 3

Öz

Kaynakça

  • Babadoğan, G., 2009, Antepfıstığı, T. C. Başbakanlık Dış Ticaret Müsteşarlığı İhracatı Geliştirme Etüd Merkezi Sektör Raporu, www.kobisektor.com/files.php?force&file= antep_520031016.pdf (25/11/2009).
  • Babadoğan, G., 2012, Antepfıstığı, T. C. Başbakanlık Dış Ticaret Müsteşarlığı İhracatı Geliştirme Etüd Merkezi Sektör Raporu, http://www.sehitkamil.gov.tr/ortak_icerik/ sehitkamil/antep_fistigi_2012.pdf(02/02/2014)
  • Bilim, H. C., 2009, Antepfıstığı Bahçelerinde Pratik Uygulamalar El Kitabı. http://www.afae.gov.tr/fistikkitap/ kitap.html (25/11/2009).
  • 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.
  • 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.
  • FAO, (2010). “Food and Agriculture Commodities,”. http://www.fao.org/es/ess/top/commodity.html
  • Fausett, L., 1994. Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall.
  • Gezginç, Y., Duman, A. D., 2004, Antepfıstığı İşleme Tekniği ve Muhafazasının Kalite Üzerine Etkisi. Gıda 29 (5): 373- 378.
  • Gonzalez, R.C. ve Woods, R.E., 1993. Digital Image Processing SE, Addison- Wesley Publishing Company, USA.
  • Jayas, D. S. and C. Karunakaran. 2005. Machine vision system in postharvest tecnology. Stewart Postharvest Rewiev, 22.
  • Keefe, P. D. 1992. A Dedicated wheat grain image analyzer. Plant Varieties and Seeds 5: 27-33.
  • Oruç, H. H., 2005, Mikotoksinler ve Tanı Yöntemleri. Uludag Univ. J. Fac. Vet. Med. 24: 105- 11
  • Öztemel E., 2003. Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık
  • Pester, T. A., P. Westra, R. L. Anderson, D. L. Lyon, S. D. Miller, P. W. Stahlman, F. E. Northam, and G. A. Wicks. 2000. Secale cereale interference and economic thresholds in winter Triticum aestivum. Weed Sci 48:720–727
  • Pérez, A. J., F. Lopez, J. V. Benlloch and S. Christensen. 2000. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 25: 197-212.
  • Trooien, T. P. and D. F. Heermann, 1992. Measurement and simulation of potato leaf area using image processing.Model development. Transactions of the ASAE 35(5):1709-1712.
  • Yaman, K., 2000. Görüntü işleme yönteminin Ankara hızlı raylı ulaşım sistemi güzergahında sefer aralıklarının optimizasyonuna yönelik olarak incelenmesi. Yayınlanmamış Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü

Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks

Yıl 2015, Cilt: 3 Sayı: 2, 86 - 89, 01.04.2015

Kadir Sabanci Murat Koklu Muhammed Fahri Unlersen

https://doi.org/10.18201/ijisae.74573
Cited By: 3

Öz

Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken by a Webcam with CCD sensor. These photos were converted to gray scale in Matlab. Afterwards, these photos were converted to binary photo format using Otsu’s Method. Then this data was used to train multi-layered neural network to complete grading.  Matlab was used for both image processing and artificial neural networks. Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.

Anahtar Kelimeler

Long type of pistachios Siirt pistachios Classification Image processing Artificial neural networks.

Kaynakça

  • Babadoğan, G., 2009, Antepfıstığı, T. C. Başbakanlık Dış Ticaret Müsteşarlığı İhracatı Geliştirme Etüd Merkezi Sektör Raporu, www.kobisektor.com/files.php?force&file= antep_520031016.pdf (25/11/2009).
  • Babadoğan, G., 2012, Antepfıstığı, T. C. Başbakanlık Dış Ticaret Müsteşarlığı İhracatı Geliştirme Etüd Merkezi Sektör Raporu, http://www.sehitkamil.gov.tr/ortak_icerik/ sehitkamil/antep_fistigi_2012.pdf(02/02/2014)
  • Bilim, H. C., 2009, Antepfıstığı Bahçelerinde Pratik Uygulamalar El Kitabı. http://www.afae.gov.tr/fistikkitap/ kitap.html (25/11/2009).
  • 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.
  • 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.
  • FAO, (2010). “Food and Agriculture Commodities,”. http://www.fao.org/es/ess/top/commodity.html
  • Fausett, L., 1994. Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall.
  • Gezginç, Y., Duman, A. D., 2004, Antepfıstığı İşleme Tekniği ve Muhafazasının Kalite Üzerine Etkisi. Gıda 29 (5): 373- 378.
  • Gonzalez, R.C. ve Woods, R.E., 1993. Digital Image Processing SE, Addison- Wesley Publishing Company, USA.
  • Jayas, D. S. and C. Karunakaran. 2005. Machine vision system in postharvest tecnology. Stewart Postharvest Rewiev, 22.
  • Keefe, P. D. 1992. A Dedicated wheat grain image analyzer. Plant Varieties and Seeds 5: 27-33.
  • Oruç, H. H., 2005, Mikotoksinler ve Tanı Yöntemleri. Uludag Univ. J. Fac. Vet. Med. 24: 105- 11
  • Öztemel E., 2003. Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık
  • Pester, T. A., P. Westra, R. L. Anderson, D. L. Lyon, S. D. Miller, P. W. Stahlman, F. E. Northam, and G. A. Wicks. 2000. Secale cereale interference and economic thresholds in winter Triticum aestivum. Weed Sci 48:720–727
  • Pérez, A. J., F. Lopez, J. V. Benlloch and S. Christensen. 2000. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 25: 197-212.
  • Trooien, T. P. and D. F. Heermann, 1992. Measurement and simulation of potato leaf area using image processing.Model development. Transactions of the ASAE 35(5):1709-1712.
  • Yaman, K., 2000. Görüntü işleme yönteminin Ankara hızlı raylı ulaşım sistemi güzergahında sefer aralıklarının optimizasyonuna yönelik olarak incelenmesi. Yayınlanmamış Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Kadir Sabanci

Murat Koklu

Muhammed Fahri Unlersen

Yayımlanma Tarihi 1 Nisan 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 3 Sayı: 2

Kaynak Göster

APA Sabanci, K., Koklu, M., & Unlersen, M. F. (2015). Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 3(2), 86-89. https://doi.org/10.18201/ijisae.74573
AMA Sabanci K, Koklu M, Unlersen MF. Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering. Nisan 2015;3(2):86-89. doi:10.18201/ijisae.74573
Chicago Sabanci, Kadir, Murat Koklu, ve Muhammed Fahri Unlersen. “Classification of Siirt and Long Type Pistachios (Pistacia Vera L.) by Artificial Neural Networks”. International Journal of Intelligent Systems and Applications in Engineering 3, sy. 2 (Nisan 2015): 86-89. https://doi.org/10.18201/ijisae.74573.
EndNote Sabanci K, Koklu M, Unlersen MF (01 Nisan 2015) Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering 3 2 86–89.
IEEE K. Sabanci, M. Koklu, ve M. F. Unlersen, “Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks”, International Journal of Intelligent Systems and Applications in Engineering, c. 3, sy. 2, ss. 86–89, 2015, doi: 10.18201/ijisae.74573.
ISNAD Sabanci, Kadir vd. “Classification of Siirt and Long Type Pistachios (Pistacia Vera L.) by Artificial Neural Networks”. International Journal of Intelligent Systems and Applications in Engineering 3/2 (Nisan 2015), 86-89. https://doi.org/10.18201/ijisae.74573.
JAMA Sabanci K, Koklu M, Unlersen MF. Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering. 2015;3:86–89.
MLA Sabanci, Kadir vd. “Classification of Siirt and Long Type Pistachios (Pistacia Vera L.) by Artificial Neural Networks”. International Journal of Intelligent Systems and Applications in Engineering, c. 3, sy. 2, 2015, ss. 86-89, doi:10.18201/ijisae.74573.
Vancouver Sabanci K, Koklu M, Unlersen MF. Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering. 2015;3(2):86-9.

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