Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Poultry Science (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2022000400504 |
Resumo: | ABSTRACT In recent years, egg production has had an intense growth in Brazil, and Brazilian egg consumption per capita has significantly increased in the last decade. To reduce sanitary and financial risks, decisions regarding the production and health status of the flock must be made based on objective criteria. Our aim was to determine the main “input” variables for the prediction of egg production performance in commercial laying breeder flocks using an ANN model. The software NeuroShellClassifier and NeuroShell Predictor were used to build the ANN. A total of 26 egg-production traits were selected as input variables and eight as output variables. A database of 44,120 Excel cells was generated. For the training and validation of the models, 74.9% and 25.1% of the data were used, respectively. The accuracy of the ANN models was calculated and compared using the analysis of coefficient of multiple determination (R2), mean squared error (MSE), and an assessment of uniform scatter in the residual plots. The models for the outputs “weekly egg production,” “weekly incubated egg,”, “accumulated commercial egg,” and “viability” showed an R2 greater than 0.8. Other models yielded R2 values lower than 0.8. The ANN predicts adequately eight egg-production traits in the breeders of commercial laying hens. The method is an option for data management analysis in the egg industry, providing estimates of the relative contribution of each input variable to the outputs. |
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Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder HensArtificial intelligencedata managementmathematical modelspoultry productionABSTRACT In recent years, egg production has had an intense growth in Brazil, and Brazilian egg consumption per capita has significantly increased in the last decade. To reduce sanitary and financial risks, decisions regarding the production and health status of the flock must be made based on objective criteria. Our aim was to determine the main “input” variables for the prediction of egg production performance in commercial laying breeder flocks using an ANN model. The software NeuroShellClassifier and NeuroShell Predictor were used to build the ANN. A total of 26 egg-production traits were selected as input variables and eight as output variables. A database of 44,120 Excel cells was generated. For the training and validation of the models, 74.9% and 25.1% of the data were used, respectively. The accuracy of the ANN models was calculated and compared using the analysis of coefficient of multiple determination (R2), mean squared error (MSE), and an assessment of uniform scatter in the residual plots. The models for the outputs “weekly egg production,” “weekly incubated egg,”, “accumulated commercial egg,” and “viability” showed an R2 greater than 0.8. Other models yielded R2 values lower than 0.8. The ANN predicts adequately eight egg-production traits in the breeders of commercial laying hens. The method is an option for data management analysis in the egg industry, providing estimates of the relative contribution of each input variable to the outputs.Fundacao de Apoio a Ciência e Tecnologia Avicolas2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2022000400504Brazilian Journal of Poultry Science v.24 n.4 2022reponame:Brazilian Journal of Poultry Science (Online)instname:Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA)instacron:FACTA10.1590/1806-9061-2021-1578info:eu-repo/semantics/openAccessOliveira,EBAlmeida,LGBRocha,DTFurian,TQBorges,KAMoraes,HLSNascimento,VPSalle,CTPeng2022-11-24T00:00:00Zoai:scielo:S1516-635X2022000400504Revistahttp://www.scielo.br/rbcahttps://old.scielo.br/oai/scielo-oai.php||rvfacta@terra.com.br1806-90611516-635Xopendoar:2022-11-24T00:00Brazilian Journal of Poultry Science (Online) - Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA)false |
dc.title.none.fl_str_mv |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
title |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
spellingShingle |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens Oliveira,EB Artificial intelligence data management mathematical models poultry production |
title_short |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
title_full |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
title_fullStr |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
title_full_unstemmed |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
title_sort |
Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens |
author |
Oliveira,EB |
author_facet |
Oliveira,EB Almeida,LGB Rocha,DT Furian,TQ Borges,KA Moraes,HLS Nascimento,VP Salle,CTP |
author_role |
author |
author2 |
Almeida,LGB Rocha,DT Furian,TQ Borges,KA Moraes,HLS Nascimento,VP Salle,CTP |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Oliveira,EB Almeida,LGB Rocha,DT Furian,TQ Borges,KA Moraes,HLS Nascimento,VP Salle,CTP |
dc.subject.por.fl_str_mv |
Artificial intelligence data management mathematical models poultry production |
topic |
Artificial intelligence data management mathematical models poultry production |
description |
ABSTRACT In recent years, egg production has had an intense growth in Brazil, and Brazilian egg consumption per capita has significantly increased in the last decade. To reduce sanitary and financial risks, decisions regarding the production and health status of the flock must be made based on objective criteria. Our aim was to determine the main “input” variables for the prediction of egg production performance in commercial laying breeder flocks using an ANN model. The software NeuroShellClassifier and NeuroShell Predictor were used to build the ANN. A total of 26 egg-production traits were selected as input variables and eight as output variables. A database of 44,120 Excel cells was generated. For the training and validation of the models, 74.9% and 25.1% of the data were used, respectively. The accuracy of the ANN models was calculated and compared using the analysis of coefficient of multiple determination (R2), mean squared error (MSE), and an assessment of uniform scatter in the residual plots. The models for the outputs “weekly egg production,” “weekly incubated egg,”, “accumulated commercial egg,” and “viability” showed an R2 greater than 0.8. Other models yielded R2 values lower than 0.8. The ANN predicts adequately eight egg-production traits in the breeders of commercial laying hens. The method is an option for data management analysis in the egg industry, providing estimates of the relative contribution of each input variable to the outputs. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2022000400504 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2022000400504 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-9061-2021-1578 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Fundacao de Apoio a Ciência e Tecnologia Avicolas |
publisher.none.fl_str_mv |
Fundacao de Apoio a Ciência e Tecnologia Avicolas |
dc.source.none.fl_str_mv |
Brazilian Journal of Poultry Science v.24 n.4 2022 reponame:Brazilian Journal of Poultry Science (Online) instname:Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA) instacron:FACTA |
instname_str |
Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA) |
instacron_str |
FACTA |
institution |
FACTA |
reponame_str |
Brazilian Journal of Poultry Science (Online) |
collection |
Brazilian Journal of Poultry Science (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Poultry Science (Online) - Fundação APINCO de Ciência e Tecnologia Avícolas (FACTA) |
repository.mail.fl_str_mv |
||rvfacta@terra.com.br |
_version_ |
1754122516132003840 |