Prediction of the weight of Alaskan Pollock using image analysis

Journal of Food Science

Volumn 75, Issue 8, 2010, Pages

  Balaban, Murat O ^a   Chombeau, Melanie ^b   Cirban, Dilşat ^c   Gümüş, Bahar ^d  

^a UNIVERSITY OF ALASKA FAIRBANKS   (United States)

^b ESITPA   (France)

^c IZMIR INSTITUTE OF TECHNOLOGY   (Turkey)

^d AKDENIZ UNIVERSITY   (Turkey)

Author keywords

Image processing; View area; Weight; Zlaskan pollock

Indexed keywords

ALGORITHM; ANIMAL; ARTICLE; BODY WEIGHT; COMPARATIVE STUDY; FIN (ORGAN); FOOD INDUSTRY; GADIFORMES; GROWTH, DEVELOPMENT AND AGING; IMAGE PROCESSING; METHODOLOGY; PHOTOGRAPHY; REGRESSION ANALYSIS; TAIL; UNITED STATES;

ALASKA; ALGORITHMS; ANIMAL FINS; ANIMALS; BODY WEIGHT; FOOD-PROCESSING INDUSTRY; GADIFORMES; IMAGE PROCESSING, COMPUTER-ASSISTED; PHOTOGRAPHY; REGRESSION ANALYSIS; TAIL;

POLLACHIUS; THERAGRA; THERAGRA CHALCOGRAMMA;

EID: 77958609033     PISSN: 00221147     EISSN: 17503841     Source Type: Journal    
DOI: 10.1111/j.1750-3841.2010.01813.x     Document Type: Article

References (17)

1. Balaban MO, Bergmann Y, Yearalan S, Otwell WS. 1994. Determination of count and uniformity ratio of shrimp by machine vision. J Aquat Food Prod Technol 3(3):43-58.
2. Balaban MO, Ünal-Şengör GF, Gil-Soriano M, Guillén-Ruiz E. 2010. Using image analysis to predict the weight of Alaskan salmon of different species. J Food Sci 75(3):E157-162.
3. Croft EA, de Silva CW, Kurnianto S. 1996. Sensor Technology integration in an intelligent machine for Herring Roe Grading. IEEE/ASME TransMechatron 1(3):204-15.
4. Damar S, Yagiz Y, Balaban MO, Ural S, Oliveira ACM, Crapo CA. 2006. Prediction of oyster volume and weight using machine vision. J Aquat Food Prod Technol 15(4):5-17.
5. Dorn M, Aydin K, Barbeaux S, Guttormsen M, Megrey B, Spalinger K, Wilkins M. 2009. Assessment of the Walleye Pollock Stock in the Gulf of Alaska. North Pacific Groundfish Stock Assessment and Fishery Evaluation Reports for 2010. 2009. 61-164 p. Available from: http://www.afsc.noaa.gov/refm/docs/2009/GOApollock.pdf. Accessed May 17, 2010.
6. Du C-J, Sun D-W. 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Tech 15:230-49.
7. Eifert JD, Sanglay GC, Lee D-J, Sumner SS, Pierson MD. 2006. Prediction of raw produce surface area from weight measurement. J Food Eng 74(4):552-6.
8. Gunasekaran S. 1996. Computer vision technology for food quality assurance. Trends Food Sci Tech 7(8):245-56.
9. Kassler M, Corke P., Wong P. 1993. Automatic grading and packing of prawns. Comput Electron Agric 9:319-33.
10. Lanelli JN, Barbeaux S, Honkalehto T, Kotwicki S, Aydin K, Williamson N. 2009. Assessment of the walleye pollock stock in the Eastern Bering Sea. North Pacific Groundfish Stock Assessment and Fishery Evaluation Reports, 2010. 2009. 49-148 p. Available from: http://www.afsc.noaa.gov/refm/docs/2009/EBSpollock.pdf. Accessed May 17, 2010.
11. Luzuriaga D, Balaban MO, Yeralan S. 1997. Analysis of visual quality attributes of white shrimp by machine vision. J Food Sci 62(1):1-7.
12. Misimi E, Erikson U, Skavhaug A. 2008a. Quality grading of Atlantic salmon (Salmo salar) by computer vision. J Food Sci 73(5):E211-7.
13. Misimi E, Erikson U, Dirge H, Skavhaug A, Mathiassen JR. 2008b. Computer vision based evaluation of pre and postrigor changes in size and shape of Atlantic cod (Gadus morhua) and Atlantic salmon (Salmo salar) fillets during rigor mortis and ice storage: effects of perimortem handling stress. J Food Sci 73(2):E57-68.
14. National Marine Fisheries Service (NMFS). 2008. Fisheries of the United States 2008. Silver Spring, Md.: U.S. Dept. of Commerce.
15. Strachan NJC, Nesvabda P, Allen AR. 1990. Fish species recognition by shape analysis of images. Pattern Recog 25:539-44.
16. Strachan NJC. 1993. Length measurements of fish by computer vision. Comput Electron Agric 8:93-104.
17. Zion B, Shklyar A, Karplus I. 1999. Sorting fish by computer vision. Comput Electron Agric 23:175-87.