Artificial neural network model for water consumption prediction in dairy farms

Detalhes bibliográficos
Autor(a) principal: Osaki, Márcia Regina
Data de Publicação: 2024
Outros Autores: Palhates, Julio Cesar Pascale, Aguiar, Fernando Guimarães
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Bioscience journal (Online)
Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/68845
Resumo: This work presents a model based on artificial neural network (ANN) applied to predict water consumption in Brazilian dairy farms. Inputs were simple process data such as number of lactating cows, milk productivity, type of management, among others, with low computational cost and satisfactory data prediction. Data used for ANN training was acquired during two years from 31 farms in semi-confined dairy production. The analysis of the results was based on the following statistical models’ indicators: R2 (Coefficient of determination), BIAS (trend coefficient), MAE (mean absolute error), RMSE (Root-mean-square deviation), NRMSE (percentage of the mean of the observations) and RAE (Relative absolute error). After performing the ANN training, the results showed good accuracy to predict water consumption in Brazilian dairy farms, with an average absolute error of 28.4% being obtained. On the other hand, considering the dataset used for ANN validation, an average absolute error of 48% was obtained.
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spelling Artificial neural network model for water consumption prediction in dairy farmsLactating CowsSemi-confinedWater EfficiencyWater Meter. Agricultural SciencesThis work presents a model based on artificial neural network (ANN) applied to predict water consumption in Brazilian dairy farms. Inputs were simple process data such as number of lactating cows, milk productivity, type of management, among others, with low computational cost and satisfactory data prediction. Data used for ANN training was acquired during two years from 31 farms in semi-confined dairy production. The analysis of the results was based on the following statistical models’ indicators: R2 (Coefficient of determination), BIAS (trend coefficient), MAE (mean absolute error), RMSE (Root-mean-square deviation), NRMSE (percentage of the mean of the observations) and RAE (Relative absolute error). After performing the ANN training, the results showed good accuracy to predict water consumption in Brazilian dairy farms, with an average absolute error of 28.4% being obtained. On the other hand, considering the dataset used for ANN validation, an average absolute error of 48% was obtained.Universidade Federal de Uberlândia2024-01-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.ufu.br/index.php/biosciencejournal/article/view/6884510.14393/BJ-v40n0a2024-68845Bioscience Journal ; Vol. 40 (2024): Continuous Publication; e40009Bioscience Journal ; v. 40 (2024): Continuous Publication; e400091981-3163reponame:Bioscience journal (Online)instname:Universidade Federal de Uberlândia (UFU)instacron:UFUenghttps://seer.ufu.br/index.php/biosciencejournal/article/view/68845/37808Brazil; Contemporary Copyright (c) 2024 Márcia Regina Osaki, Julio Cesar Pascale Palhates, Fernando Guimarães Aguiarhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessOsaki, Márcia ReginaPalhates, Julio Cesar PascaleAguiar, Fernando Guimarães2024-04-03T20:47:42Zoai:ojs.www.seer.ufu.br:article/68845Revistahttps://seer.ufu.br/index.php/biosciencejournalPUBhttps://seer.ufu.br/index.php/biosciencejournal/oaibiosciencej@ufu.br||1981-31631516-3725opendoar:2024-04-03T20:47:42Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)false
dc.title.none.fl_str_mv Artificial neural network model for water consumption prediction in dairy farms
title Artificial neural network model for water consumption prediction in dairy farms
spellingShingle Artificial neural network model for water consumption prediction in dairy farms
Osaki, Márcia Regina
Lactating Cows
Semi-confined
Water Efficiency
Water Meter.
Agricultural Sciences
title_short Artificial neural network model for water consumption prediction in dairy farms
title_full Artificial neural network model for water consumption prediction in dairy farms
title_fullStr Artificial neural network model for water consumption prediction in dairy farms
title_full_unstemmed Artificial neural network model for water consumption prediction in dairy farms
title_sort Artificial neural network model for water consumption prediction in dairy farms
author Osaki, Márcia Regina
author_facet Osaki, Márcia Regina
Palhates, Julio Cesar Pascale
Aguiar, Fernando Guimarães
author_role author
author2 Palhates, Julio Cesar Pascale
Aguiar, Fernando Guimarães
author2_role author
author
dc.contributor.author.fl_str_mv Osaki, Márcia Regina
Palhates, Julio Cesar Pascale
Aguiar, Fernando Guimarães
dc.subject.por.fl_str_mv Lactating Cows
Semi-confined
Water Efficiency
Water Meter.
Agricultural Sciences
topic Lactating Cows
Semi-confined
Water Efficiency
Water Meter.
Agricultural Sciences
description This work presents a model based on artificial neural network (ANN) applied to predict water consumption in Brazilian dairy farms. Inputs were simple process data such as number of lactating cows, milk productivity, type of management, among others, with low computational cost and satisfactory data prediction. Data used for ANN training was acquired during two years from 31 farms in semi-confined dairy production. The analysis of the results was based on the following statistical models’ indicators: R2 (Coefficient of determination), BIAS (trend coefficient), MAE (mean absolute error), RMSE (Root-mean-square deviation), NRMSE (percentage of the mean of the observations) and RAE (Relative absolute error). After performing the ANN training, the results showed good accuracy to predict water consumption in Brazilian dairy farms, with an average absolute error of 28.4% being obtained. On the other hand, considering the dataset used for ANN validation, an average absolute error of 48% was obtained.
publishDate 2024
dc.date.none.fl_str_mv 2024-01-31
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/68845
10.14393/BJ-v40n0a2024-68845
url https://seer.ufu.br/index.php/biosciencejournal/article/view/68845
identifier_str_mv 10.14393/BJ-v40n0a2024-68845
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/68845/37808
dc.rights.driver.fl_str_mv Copyright (c) 2024 Márcia Regina Osaki, Julio Cesar Pascale Palhates, Fernando Guimarães Aguiar
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Márcia Regina Osaki, Julio Cesar Pascale Palhates, Fernando Guimarães Aguiar
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Brazil; Contemporary
dc.publisher.none.fl_str_mv Universidade Federal de Uberlândia
publisher.none.fl_str_mv Universidade Federal de Uberlândia
dc.source.none.fl_str_mv Bioscience Journal ; Vol. 40 (2024): Continuous Publication; e40009
Bioscience Journal ; v. 40 (2024): Continuous Publication; e40009
1981-3163
reponame:Bioscience journal (Online)
instname:Universidade Federal de Uberlândia (UFU)
instacron:UFU
instname_str Universidade Federal de Uberlândia (UFU)
instacron_str UFU
institution UFU
reponame_str Bioscience journal (Online)
collection Bioscience journal (Online)
repository.name.fl_str_mv Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)
repository.mail.fl_str_mv biosciencej@ufu.br||
_version_ 1797069065836036096

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