Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.livsci.2019.103904 http://hdl.handle.net/11449/195235 |
Resumo: | Frequent measurements of body weight (BW) in livestock systems are very important because they allow assessing growth. However, real-time monitoring of animal growth through traditional weighing scales is stressful for animals, costly and labor-intensive. Thus, the objectives of this study were to: 1) assess the predictive quality of an automated computer vision system used to predict BW and average daily gain (ADG) in beef cattle; and 2) compare different predictive approaches, including Multiple Linear Regression (MLR), Least Absolute Shrinkage and Selection Operator (LASSO), Partial Least Squares (PLS), and Artificial Neutral Networks (ANN). A total of 234 images of Nellore beef cattle were collected during the weaning, stocker and feedlot phases. First, biometric body measurements of each animal, such as body volume, area, length, and others, were performed using three-dimensional images captured with the Kinecto (R) sensor, and their respective BW were acquired using an electronic scale. Next, the biometric measurements were used as explanatory variables in the four predictive approaches (MLR, LASSO, PLS, and ANN). To evaluate prediction quality, a leave-one-out cross-validation was adopted. The ANN was the best prediction approach in terms of Root Mean Square Error of Prediction (RMSEP) and squared predictive correlation (r(2)). The results for Weaning were RMSEP = 8.6 kg and r(2) = 0.91; for Stocker phase, RMSEP = 11.4 kg and r(2) = 0.79; and for Beginning of feedlot, RMSEP = 7.7 kg and r(2) = 0.92. The ANN was also the best method for prediction of ADG, with RMSEP = 0.02 kg/d and r(2) = 0.67 for the period between Weaning and Stocker, RMSEP = 0.02 kg/d and r(2) = 0.85 for the Weaning and Beginning of Feedlot phase, RMSEP = 0.03 kg/d and r(2) = 0.80 for Weaning and Final of Feedlot phase, RMSEP = 0.10 kg/d and r(2) = 0.51 for Stocker and Beginning of feedlot phase, and RMSEP = 0.09 kg/d and r(2) = 0.82 for the Beginning and Final of feedlot phase. Overall, the results indicate that the proposed automated computer vision system can be successfully used to predict BW and ADG in real-time in beef cattle. |
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Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phasesBeef cattleComputer visionImage analysisKinect (R)Frequent measurements of body weight (BW) in livestock systems are very important because they allow assessing growth. However, real-time monitoring of animal growth through traditional weighing scales is stressful for animals, costly and labor-intensive. Thus, the objectives of this study were to: 1) assess the predictive quality of an automated computer vision system used to predict BW and average daily gain (ADG) in beef cattle; and 2) compare different predictive approaches, including Multiple Linear Regression (MLR), Least Absolute Shrinkage and Selection Operator (LASSO), Partial Least Squares (PLS), and Artificial Neutral Networks (ANN). A total of 234 images of Nellore beef cattle were collected during the weaning, stocker and feedlot phases. First, biometric body measurements of each animal, such as body volume, area, length, and others, were performed using three-dimensional images captured with the Kinecto (R) sensor, and their respective BW were acquired using an electronic scale. Next, the biometric measurements were used as explanatory variables in the four predictive approaches (MLR, LASSO, PLS, and ANN). To evaluate prediction quality, a leave-one-out cross-validation was adopted. The ANN was the best prediction approach in terms of Root Mean Square Error of Prediction (RMSEP) and squared predictive correlation (r(2)). The results for Weaning were RMSEP = 8.6 kg and r(2) = 0.91; for Stocker phase, RMSEP = 11.4 kg and r(2) = 0.79; and for Beginning of feedlot, RMSEP = 7.7 kg and r(2) = 0.92. The ANN was also the best method for prediction of ADG, with RMSEP = 0.02 kg/d and r(2) = 0.67 for the period between Weaning and Stocker, RMSEP = 0.02 kg/d and r(2) = 0.85 for the Weaning and Beginning of Feedlot phase, RMSEP = 0.03 kg/d and r(2) = 0.80 for Weaning and Final of Feedlot phase, RMSEP = 0.10 kg/d and r(2) = 0.51 for Stocker and Beginning of feedlot phase, and RMSEP = 0.09 kg/d and r(2) = 0.82 for the Beginning and Final of feedlot phase. Overall, the results indicate that the proposed automated computer vision system can be successfully used to predict BW and ADG in real-time in beef cattle.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Univ Wisconsin, Dept Anim Sci, Madison, WI 53706 USAUniv Wisconsin, Dept Biostat & Med Informat, Madison, WI 53706 USASao Paulo State Univ, Sch Vet Med & Anim Sci, BR-18618681 Botucatu, SP, BrazilSao Paulo State Univ, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, BrazilUniv Fed Lavras, Anim Sci Dept, BR-3720000 Lavras, MG, BrazilUniv Fed Triangulo Mineiro, BR-38280000 Iturama, MG, BrazilSao Paulo State Univ, Sch Vet Med & Anim Sci, BR-18618681 Botucatu, SP, BrazilSao Paulo State Univ, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, BrazilFAPESP: 2017/20812-0FAPESP: 2017/02057-0CAPES: 001Elsevier B.V.Univ WisconsinUniversidade Estadual Paulista (Unesp)Universidade Federal de Lavras (UFLA)Univ Fed Triangulo MineiroCominotte, A.Fernandes, A. F. A.Dorea, J. R. R.Rosa, G. J. M.Ladeira, M. M.van Cleef, E. H. C. B.Pereira, G. L. [UNESP]Baldassini, W. A. [UNESP]Machado Neto, O. R. [UNESP]2020-12-10T17:27:52Z2020-12-10T17:27:52Z2020-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10http://dx.doi.org/10.1016/j.livsci.2019.103904Livestock Science. Amsterdam: Elsevier, v. 232, 10 p., 2020.1871-1413http://hdl.handle.net/11449/19523510.1016/j.livsci.2019.103904WOS:000518489400005Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLivestock Scienceinfo:eu-repo/semantics/openAccess2021-10-23T07:00:34Zoai:repositorio.unesp.br:11449/195235Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:01:35.015780Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
title |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
spellingShingle |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases Cominotte, A. Beef cattle Computer vision Image analysis Kinect (R) |
title_short |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
title_full |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
title_fullStr |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
title_full_unstemmed |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
title_sort |
Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases |
author |
Cominotte, A. |
author_facet |
Cominotte, A. Fernandes, A. F. A. Dorea, J. R. R. Rosa, G. J. M. Ladeira, M. M. van Cleef, E. H. C. B. Pereira, G. L. [UNESP] Baldassini, W. A. [UNESP] Machado Neto, O. R. [UNESP] |
author_role |
author |
author2 |
Fernandes, A. F. A. Dorea, J. R. R. Rosa, G. J. M. Ladeira, M. M. van Cleef, E. H. C. B. Pereira, G. L. [UNESP] Baldassini, W. A. [UNESP] Machado Neto, O. R. [UNESP] |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Wisconsin Universidade Estadual Paulista (Unesp) Universidade Federal de Lavras (UFLA) Univ Fed Triangulo Mineiro |
dc.contributor.author.fl_str_mv |
Cominotte, A. Fernandes, A. F. A. Dorea, J. R. R. Rosa, G. J. M. Ladeira, M. M. van Cleef, E. H. C. B. Pereira, G. L. [UNESP] Baldassini, W. A. [UNESP] Machado Neto, O. R. [UNESP] |
dc.subject.por.fl_str_mv |
Beef cattle Computer vision Image analysis Kinect (R) |
topic |
Beef cattle Computer vision Image analysis Kinect (R) |
description |
Frequent measurements of body weight (BW) in livestock systems are very important because they allow assessing growth. However, real-time monitoring of animal growth through traditional weighing scales is stressful for animals, costly and labor-intensive. Thus, the objectives of this study were to: 1) assess the predictive quality of an automated computer vision system used to predict BW and average daily gain (ADG) in beef cattle; and 2) compare different predictive approaches, including Multiple Linear Regression (MLR), Least Absolute Shrinkage and Selection Operator (LASSO), Partial Least Squares (PLS), and Artificial Neutral Networks (ANN). A total of 234 images of Nellore beef cattle were collected during the weaning, stocker and feedlot phases. First, biometric body measurements of each animal, such as body volume, area, length, and others, were performed using three-dimensional images captured with the Kinecto (R) sensor, and their respective BW were acquired using an electronic scale. Next, the biometric measurements were used as explanatory variables in the four predictive approaches (MLR, LASSO, PLS, and ANN). To evaluate prediction quality, a leave-one-out cross-validation was adopted. The ANN was the best prediction approach in terms of Root Mean Square Error of Prediction (RMSEP) and squared predictive correlation (r(2)). The results for Weaning were RMSEP = 8.6 kg and r(2) = 0.91; for Stocker phase, RMSEP = 11.4 kg and r(2) = 0.79; and for Beginning of feedlot, RMSEP = 7.7 kg and r(2) = 0.92. The ANN was also the best method for prediction of ADG, with RMSEP = 0.02 kg/d and r(2) = 0.67 for the period between Weaning and Stocker, RMSEP = 0.02 kg/d and r(2) = 0.85 for the Weaning and Beginning of Feedlot phase, RMSEP = 0.03 kg/d and r(2) = 0.80 for Weaning and Final of Feedlot phase, RMSEP = 0.10 kg/d and r(2) = 0.51 for Stocker and Beginning of feedlot phase, and RMSEP = 0.09 kg/d and r(2) = 0.82 for the Beginning and Final of feedlot phase. Overall, the results indicate that the proposed automated computer vision system can be successfully used to predict BW and ADG in real-time in beef cattle. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T17:27:52Z 2020-12-10T17:27:52Z 2020-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.livsci.2019.103904 Livestock Science. Amsterdam: Elsevier, v. 232, 10 p., 2020. 1871-1413 http://hdl.handle.net/11449/195235 10.1016/j.livsci.2019.103904 WOS:000518489400005 |
url |
http://dx.doi.org/10.1016/j.livsci.2019.103904 http://hdl.handle.net/11449/195235 |
identifier_str_mv |
Livestock Science. Amsterdam: Elsevier, v. 232, 10 p., 2020. 1871-1413 10.1016/j.livsci.2019.103904 WOS:000518489400005 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Livestock Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
10 |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128740773855232 |