Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/ani13101679 http://hdl.handle.net/11449/247441 |
Resumo: | The objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect® model 1473 sensor (Microsoft Corporation, Redmond, WA, USA). Models were compared based on root mean square error estimation and concordance correlation coefficient. The predictive quality of the approaches used multiple linear regression (MLR); least absolute shrinkage and selection operator (LASSO); partial least square (PLS), and artificial neutral network (ANN) and was affected not only by the conditions (set) but also by the objective (BW vs. HCW). The most stable for BW was the ANN (Set 1: RMSEP = 19.68; CCC = 0.73; Set 2: RMSEP = 27.22; CCC = 0.66; Set 3: RMSEP = 27.23; CCC = 0.70; Set 4: RMSEP = 33.74; CCC = 0.74), which showed predictive quality regardless of the set analyzed. However, when evaluating predictive quality for HCW, the models obtained by LASSO and PLS showed greater quality over the different sets. Overall, the use of three-dimensional images was able to predict BW and HCW in Nellore cattle. |
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Repositório Institucional da UNESP |
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Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattlebeef cattlecomputer visionimage analysisKinect®modelsThe objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect® model 1473 sensor (Microsoft Corporation, Redmond, WA, USA). Models were compared based on root mean square error estimation and concordance correlation coefficient. The predictive quality of the approaches used multiple linear regression (MLR); least absolute shrinkage and selection operator (LASSO); partial least square (PLS), and artificial neutral network (ANN) and was affected not only by the conditions (set) but also by the objective (BW vs. HCW). The most stable for BW was the ANN (Set 1: RMSEP = 19.68; CCC = 0.73; Set 2: RMSEP = 27.22; CCC = 0.66; Set 3: RMSEP = 27.23; CCC = 0.70; Set 4: RMSEP = 33.74; CCC = 0.74), which showed predictive quality regardless of the set analyzed. However, when evaluating predictive quality for HCW, the models obtained by LASSO and PLS showed greater quality over the different sets. Overall, the use of three-dimensional images was able to predict BW and HCW in Nellore cattle.Department of Animal Science University of WisconsinSchool of Agricultural and Veterinarian Sciences São Paulo State University, SPDepartment of Biostatistics and Medical Informatics University of WisconsinSchool of Veterinary and Animal Science São Paulo State University, SPSchool of Agricultural and Veterinarian Sciences São Paulo State University, SPSchool of Veterinary and Animal Science São Paulo State University, SPUniversity of WisconsinUniversidade Estadual Paulista (UNESP)Cominotte, Alexandre [UNESP]Fernandes, ArthurDórea, JoãoRosa, GuilhermeTorres, Rodrigo [UNESP]Pereira, Guilherme [UNESP]Baldassini, Welder [UNESP]Machado Neto, Otávio [UNESP]2023-07-29T13:16:09Z2023-07-29T13:16:09Z2023-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/ani13101679Animals, v. 13, n. 10, 2023.2076-2615http://hdl.handle.net/11449/24744110.3390/ani131016792-s2.0-85160224349Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnimalsinfo:eu-repo/semantics/openAccess2023-07-29T13:16:10Zoai:repositorio.unesp.br:11449/247441Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:16:10Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
title |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
spellingShingle |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle Cominotte, Alexandre [UNESP] beef cattle computer vision image analysis Kinect® models |
title_short |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
title_full |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
title_fullStr |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
title_full_unstemmed |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
title_sort |
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle |
author |
Cominotte, Alexandre [UNESP] |
author_facet |
Cominotte, Alexandre [UNESP] Fernandes, Arthur Dórea, João Rosa, Guilherme Torres, Rodrigo [UNESP] Pereira, Guilherme [UNESP] Baldassini, Welder [UNESP] Machado Neto, Otávio [UNESP] |
author_role |
author |
author2 |
Fernandes, Arthur Dórea, João Rosa, Guilherme Torres, Rodrigo [UNESP] Pereira, Guilherme [UNESP] Baldassini, Welder [UNESP] Machado Neto, Otávio [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
University of Wisconsin Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Cominotte, Alexandre [UNESP] Fernandes, Arthur Dórea, João Rosa, Guilherme Torres, Rodrigo [UNESP] Pereira, Guilherme [UNESP] Baldassini, Welder [UNESP] Machado Neto, Otávio [UNESP] |
dc.subject.por.fl_str_mv |
beef cattle computer vision image analysis Kinect® models |
topic |
beef cattle computer vision image analysis Kinect® models |
description |
The objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect® model 1473 sensor (Microsoft Corporation, Redmond, WA, USA). Models were compared based on root mean square error estimation and concordance correlation coefficient. The predictive quality of the approaches used multiple linear regression (MLR); least absolute shrinkage and selection operator (LASSO); partial least square (PLS), and artificial neutral network (ANN) and was affected not only by the conditions (set) but also by the objective (BW vs. HCW). The most stable for BW was the ANN (Set 1: RMSEP = 19.68; CCC = 0.73; Set 2: RMSEP = 27.22; CCC = 0.66; Set 3: RMSEP = 27.23; CCC = 0.70; Set 4: RMSEP = 33.74; CCC = 0.74), which showed predictive quality regardless of the set analyzed. However, when evaluating predictive quality for HCW, the models obtained by LASSO and PLS showed greater quality over the different sets. Overall, the use of three-dimensional images was able to predict BW and HCW in Nellore cattle. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:16:09Z 2023-07-29T13:16:09Z 2023-05-01 |
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/10.3390/ani13101679 Animals, v. 13, n. 10, 2023. 2076-2615 http://hdl.handle.net/11449/247441 10.3390/ani13101679 2-s2.0-85160224349 |
url |
http://dx.doi.org/10.3390/ani13101679 http://hdl.handle.net/11449/247441 |
identifier_str_mv |
Animals, v. 13, n. 10, 2023. 2076-2615 10.3390/ani13101679 2-s2.0-85160224349 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Animals |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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_ |
1799965179520221184 |