On-line weight estimation of broiler carcass and cuts by a computer vision system
ABSTRACT: In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-12-01
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| Series: | Poultry Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0032579121004971 |
| _version_ | 1818935835335065600 |
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| author | Innocent Nyalala Cedric Okinda Nelson Makange Tchalla Korohou Qi Chao Luke Nyalala Zhang Jiayu Zuo Yi Khurram Yousaf Liu Chao Chen Kunjie |
| author_facet | Innocent Nyalala Cedric Okinda Nelson Makange Tchalla Korohou Qi Chao Luke Nyalala Zhang Jiayu Zuo Yi Khurram Yousaf Liu Chao Chen Kunjie |
| author_sort | Innocent Nyalala |
| collection | DOAJ |
| description | ABSTRACT: In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction system. An Active Shape Model was developed to segment the carcass into 4 cuts (drumsticks, breasts, wings, and head and neck). Five regression models were developed based on the image features for each weight estimation (carcass and its cuts). The Bayesian-ANN model outperformed all other regression models at 0.9981 R2 and 0.9847 R2 in the whole carcass and head and neck weight estimation. The RBF-SVR model surpassed all the other drumstick, breast, and wings weight prediction models at 0.9129 R2, 0.9352 R2, and 0.9896 R2, respectively. This proposed technique can be applied as a nondestructive, nonintrusive, and accurate on-line broiler carcass production system in the automation of chicken carcass and cuts weight estimation. |
| first_indexed | 2024-12-20T05:26:29Z |
| format | Article |
| id | doaj.art-0e22200fdae54ef7808d4e754cb4966f |
| institution | Directory Open Access Journal |
| issn | 0032-5791 |
| language | English |
| last_indexed | 2024-12-20T05:26:29Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj.art-0e22200fdae54ef7808d4e754cb4966f2022-12-21T19:51:51ZengElsevierPoultry Science0032-57912021-12-0110012101474On-line weight estimation of broiler carcass and cuts by a computer vision systemInnocent Nyalala0Cedric Okinda1Nelson Makange2Tchalla Korohou3Qi Chao4Luke Nyalala5Zhang Jiayu6Zuo Yi7Khurram Yousaf8Liu Chao9Chen Kunjie10College of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaDepartment of Computer Science, Cornell University, Ithaca, NY 14853-7501, USACollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR ChinaCollege of Engineering, Nanjing Agricultural University, Jiangsu 210031, PR China; Corresponding author:ABSTRACT: In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction system. An Active Shape Model was developed to segment the carcass into 4 cuts (drumsticks, breasts, wings, and head and neck). Five regression models were developed based on the image features for each weight estimation (carcass and its cuts). The Bayesian-ANN model outperformed all other regression models at 0.9981 R2 and 0.9847 R2 in the whole carcass and head and neck weight estimation. The RBF-SVR model surpassed all the other drumstick, breast, and wings weight prediction models at 0.9129 R2, 0.9352 R2, and 0.9896 R2, respectively. This proposed technique can be applied as a nondestructive, nonintrusive, and accurate on-line broiler carcass production system in the automation of chicken carcass and cuts weight estimation.http://www.sciencedirect.com/science/article/pii/S0032579121004971broiler carcassescarcass weightcomputer vision systemregression modelingstatistical modeling |
| spellingShingle | Innocent Nyalala Cedric Okinda Nelson Makange Tchalla Korohou Qi Chao Luke Nyalala Zhang Jiayu Zuo Yi Khurram Yousaf Liu Chao Chen Kunjie On-line weight estimation of broiler carcass and cuts by a computer vision system Poultry Science broiler carcasses carcass weight computer vision system regression modeling statistical modeling |
| title | On-line weight estimation of broiler carcass and cuts by a computer vision system |
| title_full | On-line weight estimation of broiler carcass and cuts by a computer vision system |
| title_fullStr | On-line weight estimation of broiler carcass and cuts by a computer vision system |
| title_full_unstemmed | On-line weight estimation of broiler carcass and cuts by a computer vision system |
| title_short | On-line weight estimation of broiler carcass and cuts by a computer vision system |
| title_sort | on line weight estimation of broiler carcass and cuts by a computer vision system |
| topic | broiler carcasses carcass weight computer vision system regression modeling statistical modeling |
| url | http://www.sciencedirect.com/science/article/pii/S0032579121004971 |
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