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...

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Autores principales:	Innocent Nyalala, Cedric Okinda, Nelson Makange, Tchalla Korohou, Qi Chao, Luke Nyalala, Zhang Jiayu, Zuo Yi, Khurram Yousaf, Liu Chao, Chen Kunjie
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	broiler carcasses carcass weight computer vision system regression modeling statistical modeling Animal culture SF1-1100
Acceso en línea:	https://doaj.org/article/0e22200fdae54ef7808d4e754cb4966f
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spelling	oai:doaj.org-article:0e22200fdae54ef7808d4e754cb4966f2021-11-24T04:21:54ZOn-line weight estimation of broiler carcass and cuts by a computer vision system0032-579110.1016/j.psj.2021.101474https://doaj.org/article/0e22200fdae54ef7808d4e754cb4966f2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0032579121004971https://doaj.org/toc/0032-5791ABSTRACT: 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.Innocent NyalalaCedric OkindaNelson MakangeTchalla KorohouQi ChaoLuke NyalalaZhang JiayuZuo YiKhurram YousafLiu ChaoChen KunjieElsevierarticlebroiler carcassescarcass weightcomputer vision systemregression modelingstatistical modelingAnimal cultureSF1-1100ENPoultry Science, Vol 100, Iss 12, Pp 101474- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	broiler carcasses carcass weight computer vision system regression modeling statistical modeling Animal culture SF1-1100
spellingShingle	broiler carcasses carcass weight computer vision system regression modeling statistical modeling Animal culture SF1-1100 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
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.
format	article
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
title	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_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_sort	on-line weight estimation of broiler carcass and cuts by a computer vision system
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0e22200fdae54ef7808d4e754cb4966f
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On-line weight estimation of broiler carcass and cuts by a computer vision system

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