SavedAdd your journal

Journals / International Journal of Intelligent Systems and Applications in Engineering / 2015 / Cilt: 3 - Sayı: 2

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

KADİR SABANCI
MURAT KÖKLÜ
MUHAMMED FAHRİ ÜNLERŞEN
Pages
86–89
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Quality is one of the important factors in agricultural products marketing.
📊
Key Findings & Data:Grading machines have great role in quality control systems.
💡
Conclusion & Impact:Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

🔖

Cite This Article

SABANCI, K., KÖKLÜ, M., ÜNLERŞEN, 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, Cilt: 3 - Sayı: 2, 86–89. https://ajindex.com/articles/270351

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. 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)
  2. 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
  3. Bilim, H. C., 2009, Antepfıstığı Bahçelerinde Pratik Uygulamalar El Kitabı. http://www.afae.gov.tr/fistikkitap/ kitap.html (25/11/2009)
  4. 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
  5. 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
  6. FAO, (2010). “Food and Agriculture Commodities,”.
  7. Fausett, L., 1994. Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall
  8. Gezginç, Y., Duman, A. D., 2004, Antepfıstığı İşleme Tekniği ve Muhafazasının Kalite Üzerine Etkisi. Gıda 29 (5): 373- 378
  9. Gonzalez, R.C. ve Woods, R.E., 1993. Digital Image Processing SE, Addison- Wesley Publishing Company, USA
  10. Jayas, D. S. and C. Karunakaran. 2005. Machine vision system in postharvest tecnology. Stewart Postharvest Rewiev, 22
  11. Keefe, P. D. 1992. A Dedicated wheat grain image analyzer. Plant Varieties and Seeds 5: 27-33
  12. Oruç, H. H., 2005, Mikotoksinler ve Tanı Yöntemleri. Uludag Univ. J. Fac. Vet. Med. 24: 105- 11 [13] Öztemel E., 2003. Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık [14] 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 [15] 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
  13. 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.