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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
08/02/2021 |
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Data da última atualização: |
06/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CAIRO, F. C.; PEREIRA, L. G. R.; CAMPOS, M. M.; TOMICH, T. R.; COELHO, S. G.; LAGE, C. F. A.; FONSECA, A. P.; BORGES, A. M.; ALVES, B. R. C.; DOREA, J. R. R. |
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Afiliação: |
F. C. Cairo, Universidade Estadual do Sudoeste da Bahia, Itapetinga, BA; LUIZ GUSTAVO RIBEIRO PEREIRA, CNPGL; MARIANA MAGALHAES CAMPOS, CNPGL; THIERRY RIBEIRO TOMICH, CNPGL; S. G. Coelho, UFMG; C. F. A. Lage, UFMG; A. P. Fonseca, UFMG; A. M. Borges, UFMG; B. R. C. Alves, University of Nevada, Reno, USA; J. R. R. Dorea, University of Wisconsin, Madison, USA. |
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Título: |
Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 179, 105855, 2020. |
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DOI: |
https://doi.org/10.1016/j.compag.2020.105855 |
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Idioma: |
Inglês |
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Conteúdo: |
The recent advances in sensor technology have allowed accurate predictions of estrus events using animal behavior information. Behavioral variables generated by electronic feed and water bins have not been explored as potential predictors for estrus detection. The objectives of this study were: (i) to evaluate the effect of estrus expression on feed intake and animal behavior (feeding and drinking) and (ii) to develop and evaluate predictive approaches to detect estrus expression using electronic feed and water bins data. Feed intake, animal behavior, and estrus events were measured in 57 Holstein × Gyr heifers (374 ± 21.2 kg and 22.6 ± 0.60 months). Previous to each estrus event, the following covariates were computed: total feed intake (FI, as-fed basis), number of visits at the feed bins (VF) and water bins (VW), time spent eating (TE), and time spent drinking water (TD). Three predictive approaches were evaluated: Generalized Linear Models (GLM), Artificial Neural Network (ANN), and Random Forest (RF). Twelve covariate sets were established to investigate: (ii.a) the prediction quality for estrus detection when long (−174 to 0 h) or short (−24 to 0 h) time series were used as predictors (6 h of time window, with estrus event at 0 h); (ii.b) the ability of machine learning algorithms to predict estrus 6 and 12 h in advance; and (ii.c) the predictive quality for estrus detection when only feeding and drinking behavior data (without intake variables) were included as predictors. The predictive approaches were evaluated through Leave-One-Out Cross-validation. Estrus events altered feeding and drinking behavior patterns, and feed intake. ANN, RF, and GLM presented similar accuracies within covariate sets. There was no benefit of using longer time series for estrus detection. Earlier detection of estrus event (6 and 12 h in advance) reduced model accuracy compared to predictions performed at 0 h. However, ANN and RF showed accuracy values ranging between 75.7% and 96.5%, which indicates a great potential for early estrus detection. The exclusion of feed intake data of the covariate sets did not reduce the accuracy, sensitivity, and specificity of the models for estrus detection. These findings suggest that behavioral data can early predict estrus events, which could be incorporated in sensor technologies capable of generating behavioral information, such as electronic bins, wearable sensors, and computer vision systems. MenosThe recent advances in sensor technology have allowed accurate predictions of estrus events using animal behavior information. Behavioral variables generated by electronic feed and water bins have not been explored as potential predictors for estrus detection. The objectives of this study were: (i) to evaluate the effect of estrus expression on feed intake and animal behavior (feeding and drinking) and (ii) to develop and evaluate predictive approaches to detect estrus expression using electronic feed and water bins data. Feed intake, animal behavior, and estrus events were measured in 57 Holstein × Gyr heifers (374 ± 21.2 kg and 22.6 ± 0.60 months). Previous to each estrus event, the following covariates were computed: total feed intake (FI, as-fed basis), number of visits at the feed bins (VF) and water bins (VW), time spent eating (TE), and time spent drinking water (TD). Three predictive approaches were evaluated: Generalized Linear Models (GLM), Artificial Neural Network (ANN), and Random Forest (RF). Twelve covariate sets were established to investigate: (ii.a) the prediction quality for estrus detection when long (−174 to 0 h) or short (−24 to 0 h) time series were used as predictors (6 h of time window, with estrus event at 0 h); (ii.b) the ability of machine learning algorithms to predict estrus 6 and 12 h in advance; and (ii.c) the predictive quality for estrus detection when only feeding and drinking behavior data (without intake variables) were includ... Mostrar Tudo |
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Palavras-Chave: |
Artificial neural network; Heat detection; Machine learning; Precision livestock; Random forest. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03409naa a2200301 a 4500
001 2129867
005 2024-02-06
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2020.105855$2DOI
100 1 $aCAIRO, F. C.
245 $aApplying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers.$h[electronic resource]
260 $c2020
520 $aThe recent advances in sensor technology have allowed accurate predictions of estrus events using animal behavior information. Behavioral variables generated by electronic feed and water bins have not been explored as potential predictors for estrus detection. The objectives of this study were: (i) to evaluate the effect of estrus expression on feed intake and animal behavior (feeding and drinking) and (ii) to develop and evaluate predictive approaches to detect estrus expression using electronic feed and water bins data. Feed intake, animal behavior, and estrus events were measured in 57 Holstein × Gyr heifers (374 ± 21.2 kg and 22.6 ± 0.60 months). Previous to each estrus event, the following covariates were computed: total feed intake (FI, as-fed basis), number of visits at the feed bins (VF) and water bins (VW), time spent eating (TE), and time spent drinking water (TD). Three predictive approaches were evaluated: Generalized Linear Models (GLM), Artificial Neural Network (ANN), and Random Forest (RF). Twelve covariate sets were established to investigate: (ii.a) the prediction quality for estrus detection when long (−174 to 0 h) or short (−24 to 0 h) time series were used as predictors (6 h of time window, with estrus event at 0 h); (ii.b) the ability of machine learning algorithms to predict estrus 6 and 12 h in advance; and (ii.c) the predictive quality for estrus detection when only feeding and drinking behavior data (without intake variables) were included as predictors. The predictive approaches were evaluated through Leave-One-Out Cross-validation. Estrus events altered feeding and drinking behavior patterns, and feed intake. ANN, RF, and GLM presented similar accuracies within covariate sets. There was no benefit of using longer time series for estrus detection. Earlier detection of estrus event (6 and 12 h in advance) reduced model accuracy compared to predictions performed at 0 h. However, ANN and RF showed accuracy values ranging between 75.7% and 96.5%, which indicates a great potential for early estrus detection. The exclusion of feed intake data of the covariate sets did not reduce the accuracy, sensitivity, and specificity of the models for estrus detection. These findings suggest that behavioral data can early predict estrus events, which could be incorporated in sensor technologies capable of generating behavioral information, such as electronic bins, wearable sensors, and computer vision systems.
653 $aArtificial neural network
653 $aHeat detection
653 $aMachine learning
653 $aPrecision livestock
653 $aRandom forest
700 1 $aPEREIRA, L. G. R.
700 1 $aCAMPOS, M. M.
700 1 $aTOMICH, T. R.
700 1 $aCOELHO, S. G.
700 1 $aLAGE, C. F. A.
700 1 $aFONSECA, A. P.
700 1 $aBORGES, A. M.
700 1 $aALVES, B. R. C.
700 1 $aDOREA, J. R. R.
773 $tComputers and Electronics in Agriculture$gv. 179, 105855, 2020.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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| Registros recuperados : 6 | |
| 2. |  | CAIRO, F. C; OLIVEIRA, B. S.; SANTOS, R. D. dos; NEVES, A. L. A.; AZEVEDO, J. A. G.; NASCIMENTO, L. S.; VALESCO, F. O.; PEREIRA, L. G. R. Consumo e digestibilidade aparente das silagens de seis cultivares de sorgo em ovinos. In: CONGRESSO NORDESTINO DE PRODUÇÃO ANIMAL, 12.; SIMPÓSIO NORDESTINO DE ALIMENTAÇÃO DE RUMINANTES, 18.; SIMPÓSIO NORDESTINO DE SISTEMAS DE PRODUÇÃO DE RUMINANTES, 5.; SIMPÓSIO DE PRODUÇÃO ANIMAL DO VALE DO SÃO FRANCISCO, 6.; SIMPÓSIO NORDESTINO DE FORRAGICULTURA E PASTAGENS, 5.; SIMPÓSIO NORDESTINO DE CONSERVAÇÃO E UTILIZAÇÃO DE RECURSOS GENÉTICOS ANIMAIS, 6.; SIMPÓSIO NORDESTINO SOBRE AMBIÊNCIA, BEM-ESTAR ANIMAL E CONVIVÊNCIA COM O SEMIÁRIDO, 1.; SIMPÓSIO IMPORTÂNCIA DAS PASTAGENS NATIVAS PARA A SUSTENTABILIDADE PECUÁRIA NO In: In: In: SEMIÁRIDO; FÓRUM DE COORDENADORES DE PROGRAMAS DE PÓS-GRADUAÇÃO EM ZOOTECNIA E RECURSOS PESQUEIROS DO NORDESTE, 7.; FÓRUM DE INTEGRAÇÃO ENTRE A ACADEMIA, AGENTES DE EXTENSÃO RURAL E PRODUTORES, 1. 2018, Juazeiro, BA. Construindo pontes entre o ensino, a pesquisa e a extensão: anais. Petrolina: Univasf: Embrapa Semiárido: Instituto Federal de Educação, Ciência e Tecnologia, Sertão de Pernambuco, 2017. p 728-730. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Semiárido. |
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| 3. | | CAIRO, F. C.; PEREIRA, L. G. R.; CAMPOS, M. M.; TOMICH, T. R.; COELHO, S. G.; LAGE, C. F. A.; FONSECA, A. P.; BORGES, A. M.; ALVES, B. R. C.; DOREA, J. R. R. Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers. Computers and Electronics in Agriculture, v. 179, 105855, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 4. | | RIBEIRO, A. K. do C.; CAIRO, F. C.; ALBUQUERQUE, B. S. F.; SOARES, G. O.; COMINATO. V.; PEREIRA, B. P.; PAIXÃO, R. Q. da; MACHADO, F. S.; PEREIRA, L. G. R.; TOMICH, T. R.; CAMPOS, M. M. Detecção de estro através de sistema de cochos e bebedouros eletrônicos em novilhas leiteiras. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 25., 2021, Juiz de Fora. Anais... Juiz de Fora: Embrapa Gado de Leite, 2021. (Embrapa Gado de Leite. Documentos, 261).| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 5. | | SOARES, G. O.; CAIRO, F. C.; RIBEIRO, A. K. do C.; ALBUQUERQUE, B. S. P.; PEREIRA, B. P.; PAIXÃO, R. Q. da; COMINATO, V.; CAMPOS, M. M.; PEREIRA, L. G. R.; TOMICH, T. R.; MACHADO, F. S. Consumo e comportamento alimentar de novilhas leiteiras em estro. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 25., 2021, Juiz de Fora. Anais... Juiz de Fora: Embrapa Gado de Leite, 2021. (Embrapa Gado de Leite. Documentos, 261).| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 6. | | GUIMARÃES, G. S.; AZEVEDO, J. A. G.; CAIRO, F. C.; SILVA, C. S. da; SOUZA, L. L.; NUNES, F. dos S.; CARVALHO, G. G. P. de; ARAUJO, G. G. L. de; SILVA, R. R. Proportions of concentrate and rehydrated ground grain corn silage at different storage times for better use of starch by lambs. Tropical Animal Health and Production, v. 54, n. 4, 297, 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 6 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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