Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system
Summary: Improving carcass portion yields (e.g., breast meat) is a major goal of modern turkey breeding and traditionally requires manual collection of portion weights. This can be a labor-intensive process considering the large amount of data needed to be useful for breeding companies. Recently, th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Applied Poultry Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1056617124001041 |
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| author | Shai Barbut Emily M. Leishman Ryley J. Vanderhout Benjamin J. Wood Christine F. Baes |
| author_facet | Shai Barbut Emily M. Leishman Ryley J. Vanderhout Benjamin J. Wood Christine F. Baes |
| author_sort | Shai Barbut |
| collection | DOAJ |
| description | Summary: Improving carcass portion yields (e.g., breast meat) is a major goal of modern turkey breeding and traditionally requires manual collection of portion weights. This can be a labor-intensive process considering the large amount of data needed to be useful for breeding companies. Recently, there has been increasing interest in using computer vision systems to assess parameters such as size, weight, volume, and grade of poultry meat. The present study developed mathematical equations to predict turkeys’ (4,000) meat yield using a non-invasive real-time 2D carcass imaging system. Although our breast meat models proved to be good, the thigh and drum models did not demonstrate a high correlation between observed and predicted weights probably due to the orientation of the image and any potential shifts made during image capture. These results represent a first step in developing prediction models for valuable turkey carcass portions using practical imaging systems. Further investigations need to take place to demonstrate this system can be more fruitful than simply predicting portion weight off live weight and help the industry to better collect phenotypes in a cost-effective manner. |
| format | Article |
| id | doaj-art-011638ef2e6a4cf884679674bf84e9dc |
| institution | Kabale University |
| issn | 1056-6171 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Applied Poultry Research |
| spelling | doaj-art-011638ef2e6a4cf884679674bf84e9dc2025-01-22T05:41:06ZengElsevierJournal of Applied Poultry Research1056-61712025-03-01341100506Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging systemShai Barbut0Emily M. Leishman1Ryley J. Vanderhout2Benjamin J. Wood3Christine F. Baes4Department of Food Science, University of Guelph, 50 Stone Road E., Guelph, Ontario N1G 2W1, Canada; Adaptation Physiology, Wageningen University, 6708, the Netherlands; Corresponding author at: Department of Food Science, University of Guelph, 50 Stone Road E., Guelph, Ontario N1G 2W1, Canada.Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, CanadaHybrid Turkeys, Suite C, 650 Riverbend Drive, Kitchener, Ontario N2K 3S2, CanadaSchool of Veterinary Science, University of Queensland, Gatton, Queensland 4343, AustraliaDepartment of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, Canada; Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern 3001, SwitzerlandSummary: Improving carcass portion yields (e.g., breast meat) is a major goal of modern turkey breeding and traditionally requires manual collection of portion weights. This can be a labor-intensive process considering the large amount of data needed to be useful for breeding companies. Recently, there has been increasing interest in using computer vision systems to assess parameters such as size, weight, volume, and grade of poultry meat. The present study developed mathematical equations to predict turkeys’ (4,000) meat yield using a non-invasive real-time 2D carcass imaging system. Although our breast meat models proved to be good, the thigh and drum models did not demonstrate a high correlation between observed and predicted weights probably due to the orientation of the image and any potential shifts made during image capture. These results represent a first step in developing prediction models for valuable turkey carcass portions using practical imaging systems. Further investigations need to take place to demonstrate this system can be more fruitful than simply predicting portion weight off live weight and help the industry to better collect phenotypes in a cost-effective manner.http://www.sciencedirect.com/science/article/pii/S10566171240010412D imagingCarcassTurkeyWeight prediction |
| spellingShingle | Shai Barbut Emily M. Leishman Ryley J. Vanderhout Benjamin J. Wood Christine F. Baes Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system Journal of Applied Poultry Research 2D imaging Carcass Turkey Weight prediction |
| title | Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system |
| title_full | Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system |
| title_fullStr | Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system |
| title_full_unstemmed | Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system |
| title_short | Applied research note: Predicting carcass portion weights for purebred turkey (Meleagris gallopavo) lines using a 2D imaging system |
| title_sort | applied research note predicting carcass portion weights for purebred turkey meleagris gallopavo lines using a 2d imaging system |
| topic | 2D imaging Carcass Turkey Weight prediction |
| url | http://www.sciencedirect.com/science/article/pii/S1056617124001041 |
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