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Proceedings Paper

Development and evaluation of a vision based poultry debone line monitoring system
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Paper Abstract

Efficient deboning is key to optimizing production yield (maximizing the amount of meat removed from a chicken frame while reducing the presence of bones). Many processors evaluate the efficiency of their deboning lines through manual yield measurements, which involves using a special knife to scrape the chicken frame for any remaining meat after it has been deboned. Researchers with the Georgia Tech Research Institute (GTRI) have developed an automated vision system for estimating this yield loss by correlating image characteristics with the amount of meat left on a skeleton. The yield loss estimation is accomplished by the system’s image processing algorithms, which correlates image intensity with meat thickness and calculates the total volume of meat remaining. The team has established a correlation between transmitted light intensity and meat thickness with an R2 of 0.94. Employing a special illuminated cone and targeted software algorithms, the system can make measurements in under a second and has up to a 90-percent correlation with yield measurements performed manually. This same system is also able to determine the probability of bone chips remaining in the output product. The system is able to determine the presence/absence of clavicle bones with an accuracy of approximately 95 percent and fan bones with an accuracy of approximately 80%. This paper describes in detail the approach and design of the system, results from field testing, and highlights the potential benefits that such a system can provide to the poultry processing industry.

Paper Details

Date Published: 29 May 2013
PDF: 11 pages
Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V, 872107 (29 May 2013); doi: 10.1117/12.2018120
Show Author Affiliations
Colin T. Usher, Georgia Tech Research Institute (United States)
W. D. R. Daley, Georgia Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 8721:
Sensing for Agriculture and Food Quality and Safety V
Moon S. Kim; Shu-I Tu; Kuanglin Chao, Editor(s)

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