Methods of manipulation and image acquisition of natural products on the example of cereal grains

• Autorzy: Lampa P. , Mrzygłód M. , Reiner J.
• Języki publikacji: EN

Abstrakty

EN

Due to growing requirements concerning quality of products in food industry, it becomes important to introduce new, efficient, objective and repeatable automated methods of quality inspection. Agricultural products, such as cereal grains, are particularly difficult for automated inspection. This paper presents the results of experimental studies on the development of automated methods of manipulation and image acquisition of cereals, which can be used in machine vision systems for quality evaluation. Experimental studies were carried out on samples of brewing barley. The proof-of-concept evaluation of several methods was performed, on the basis of which the solution was chosen, based on the interaction of the screw and vibration feeders and bilateral image acquisition on moving transparent surface.

Słowa kluczowe

EN

machine vision; manipulation; automatic optical inspection; food safety and quality; grain

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2016
• Tom: Vol. 45, no. 3
• Strony: 339--353
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Lampa P.

• Wroclaw University of Technology, Faculty of Mechanical Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wrocaw, Poland

autor

Mrzygłód M.

• Wroclaw University of Technology, Faculty of Mechanical Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wrocaw, Poland

autor

Reiner J.

• Wroclaw University of Technology, Faculty of Mechanical Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wrocaw, Poland

Bibliografia

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• [2] Chen, Y. R., Chao, K. and Kim, M. S. (2002) Machine vision technology for agricultural applications. Computers and Electronics in Agriculture 36 (2–3), 173–191.
• [3] Choudhary, R., Paliwal, J. and Jayas, D. S. (2008) Classification of cereal grains using wavelet, morphological, colour, and textural features of nontouching kernel images. Biosystems Engineering 99 (3), 330–337.
• [4] Hug, A. (2013) Sorting and inspection apparatus and method with determination of product velocity. US Patent 14/129,333, January 3.
• [5] Kajiura, T., Oita, N., Abe, J. and Sugiyama, S. (1989) Granule inspection apparatus. US Patent 07/089,302, May 16.
• [6] Majumdar, S. and Jayas, D. S. (2000) Classification of cereal grains using machine vision: IV. Combined morphology, color, and texture models. Transactions of the ASAE 43 (6), 1689.
• [7] Mebatsion, H. K., Paliwal, J. and Jayas, D. S. (2013) Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Computers and Electronics in Agriculture 90, 99–105.
• [8] Paliwal, J., Visen, N. S. and Jayas, D. S. (2001) Evaluation of neural network architectures for cereal grain classification using morphological features. Journal of Agricultural Engineering Research 79(4), 361–370.
• [9] Polish Standard PN-R-74110 (1998) Barley - Test methods. Reiner, J., Mrzyglod, M. and Tryba, D. (2008) Laser measurement of throbing and flatness of the circural saw disks (in Polish). Napędy i Sterowanie 10 (6), 39–42.
• [10] Reiner, J. and Mrzyglod, M. (2010) Selected machine vision methods for inspection of metal parts (in Polish). Zeszyty Naukowe Politechniki Poznanskiej. Budowa Maszyn i Zarzadzanie Produkcja¸ 14, 71–76.
• [11] Reiner, J., Mrzyglod, M., Zmywaczyk, J., Trzyna, M. and Jaremek, H. (2014) Quality inspection of termochromic liquid crystals calibration for medical imaging (in Polish). Mechanik 87 (8-9), 326–336.
• [12] Szczypinski, P. M., Klepaczko, A. and Zapotoczny, P. (2015) Identifying barley varieties by computer vision. Computers and Electronics in Agriculture 110, 1–8.
• [13] Weiss, M. and Armstrong, B. (2007) Seed tray for digital image analysis of grain and the like. US Patent 10/217,264, February 27.
• [14] Zapotoczny, P. (2011) Discrimination of wheat grain varieties using image analysis and neural networks. Part I. Single kernel texture. Journal of Cereal Science 54 (1), 60–68.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).