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Detection of Defective Hazelnuts by Image Processing and Machine Learning

Oğuzhan KIVRAK, Mustafa Zahid Gürbüz

Abstract

Hazelnut, is an oily food, that contains nutrients which is important for human health. The quality of the hazelnuts can be varied by internal and external factors such as the temperature of the environment, relative humidity of the environment, harvesting, drying and storage conditions, pesticide and mold growth. After harvesting, a machine (patoz machine) is used to separate the other shell of hazelnut. The patoz can mix the poor-quality hazelnut into the solid hazelnut, damage the shell and also discard impurities such as iron and stones during extraction. The average amount of impurities in raw hazelnuts at the time of 40 kg/ton. The average transaction for a factory is 200 tons per day, which can result in significant financial losses. The aim of this project is to separate intact hazelnuts from damaged or imperfect hazelnuts and impurities by using image processing and artificial intelligence. 1000 number of photos of hazelnut that obtained from the patoz machine were taken. They uploaded to the system. The system used supervised learning method. In this paper, the obtained results are very satisfactory

Keywords

Hazelnut, image processing, machine learning

Volume 4, No 3, SUPPLEMENT I of SYMPOSIUM ARTICLES, pp 100-106, 2019



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References
  • Gonenc, S.,Tanrıvermiş, H.,Bülbül, M. (2006). Economic Assessment of Hazelnut Production and the Importance of Supply Management Approaches in Turkey. Journal of Agriculture and Rural Development in the Tropics and Subtropics, 107(1), 19–3.
  • Moscetti, R., Saeys, W., Keresztes, J. C., Goodarzi, M., Cecchini, M., Danilo, M., Massantini, R. (2015). Hazelnut Quality Sorting Using High Dynamic Range Short-Wave Infrared Hyperspectral Imaging. Food and Bioprocess Technology, Volume 8, Issue 7, 1593–1604.
  • Zhang, X., Zhang, Li, D. (2019). Transmission Line Abnormal Target Detection Based on Machine Learning Yolo V3. International Conference on Advanced Mechatronic Systems (ICAMechS). DOI: 10.1109/ICAMechS.2019.8861617
  • Github : htps://github.com/tzutalin/labelImg , 2019.
  • McAndrew, A. (2016). A Computational Introduction to Digital Image Process Second Editon. © 2016 by Taylor & Francis Group, LLC, International Standard Book Number-13: 978-1-4822-4733-6 (eBook - PDF)
  • Kim, I., Lee, C. (2013). An Efficient Gradient-based Approach to Optimizing Average Precision Through Maximal Figure-of-Merit Learning, J Sign Process Syst., 74:285–295, DOI 10.1007/s11265-013-0748-0
  • Beitzel S. M., Jensen E.C., Frieder O. (2009). MAP. In: LIU L., ÖZSU M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA.