Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Wisconsin-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de Lavras (UFLA)-
Autor(es): dc.contributorUniv Fed Triangulo Mineiro-
Autor(es): dc.creatorCominotte, A.-
Autor(es): dc.creatorFernandes, A. F. A.-
Autor(es): dc.creatorDorea, J. R. R.-
Autor(es): dc.creatorRosa, G. J. M.-
Autor(es): dc.creatorLadeira, M. M.-
Autor(es): dc.creatorvan Cleef, E. H. C. B.-
Autor(es): dc.creatorPereira, G. L. [UNESP]-
Autor(es): dc.creatorBaldassini, W. A. [UNESP]-
Autor(es): dc.creatorMachado Neto, O. R. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:05:17Z-
Data de disponibilização: dc.date.available2022-02-22T00:05:17Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.livsci.2019.103904-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/195235-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/195235-
Descrição: dc.descriptionFrequent 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionUniv Wisconsin, Dept Anim Sci, Madison, WI 53706 USA-
Descrição: dc.descriptionUniv Wisconsin, Dept Biostat & Med Informat, Madison, WI 53706 USA-
Descrição: dc.descriptionSao Paulo State Univ, Sch Vet Med & Anim Sci, BR-18618681 Botucatu, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, Brazil-
Descrição: dc.descriptionUniv Fed Lavras, Anim Sci Dept, BR-3720000 Lavras, MG, Brazil-
Descrição: dc.descriptionUniv Fed Triangulo Mineiro, BR-38280000 Iturama, MG, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Sch Vet Med & Anim Sci, BR-18618681 Botucatu, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, Brazil-
Descrição: dc.descriptionFAPESP: 2017/20812-0-
Descrição: dc.descriptionFAPESP: 2017/02057-0-
Descrição: dc.descriptionCAPES: 001-
Formato: dc.format10-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier B.V.-
Relação: dc.relationLivestock Science-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectBeef cattle-
Palavras-chave: dc.subjectComputer vision-
Palavras-chave: dc.subjectImage analysis-
Palavras-chave: dc.subjectKinect (R)-
Título: dc.titleAutomated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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