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| Registros recuperados : 42 | |
| 3. |  | ALVES, B. R. C.; CARDOSO, R. C.; DOAN, R.; Gary L. Williams; DINDOT, S. V.; AMSTALDEN, M. Nutritional Programming of Accelerated Puberty in Heifers: Alterations in DNA Methylation in the Arcuate Nucleus. In: ANNUAL MEETING OF THE SOCIETY FOR THE STUDY OF REPRODUCTION, 48., 2015, San Juan, Puerto Rico, USA. Evolution of sex: abstracts. San Juan: SSR, 2015. p. 117| Biblioteca(s): Embrapa Gado de Leite. |
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| 4. | | SANTOS, G. B.; CARVALHO, B. C. de; VARGAS, M. W.; MACHADO, F. S.; PIRES, M. de F. A.; VARAGO, F. C.; ALVES, B. R. C.; CAMPOS, M. M. Avaliação automatizada do comportamento ingestivo de vacas da raça Holandês com metrite. In: CONGRESSO INTERNACIONAL DO LEITE, 13., 2015, Porto Alegre. Anais... Juiz de Fora: Embrapa Gado de Leite, 2015. 4 p.| Biblioteca(s): Embrapa Gado de Leite. |
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| 5. | | YOUWEN, Z.; NEIL, O.; SCARPA, J. O.; ALVES, B. R. C.; WELSH JR, T. J.; CARDOSO, R. C.; WILLIAMS, G. L. Perinatal nutrition alters NPY neuronal projection do GuRH neurons and loptin receptor expression in the croid plexus of prepubertal heifers. In: ANNUAL MEETING OF THE SOCIETY FOR THE STUDY OF REPRODUTION, 50., 2017. Proceedings... Washington: [s.n.], 2017.| Biblioteca(s): Embrapa Gado de Leite. |
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| 6. | | SANTOS, G. B.; CARVALHO, B. C. de; VARGAS, M. W.; MACHADO, F. S.; PIRES, M. de F. A.; VARAGO, F. C.; ALVES, B. R. C.; CAMPOS, M. M. Perfil metabólico de vacas da raça Holandês com e sem metrite no pós-parto. In: CONGRESSO INTERNACIONAL DO LEITE, 13., 2015, Porto Alegre. Anais... Juiz de Fora: Embrapa Gado de Leite, 2015. 4 p.| Biblioteca(s): Embrapa Gado de Leite. |
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| 7. | | ROSA, P. M. da S.; CAMARGO, A. J. DOS R.; SERAPIÃO, R, V.; ALVES, B. R. C.; NOGUEIRA, G. DE. P.; OLIVEIRA, C. S. Perfil de secreção de hormônio luteinizante em novilhas pré-púberes da raça Gir, submetidas a protocolo de indução de ovulação. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 20. 2017, 4 p. Anais... Juiz de Fora/MG. Embrapa Gado de Leite.| Biblioteca(s): Embrapa Gado de Leite. |
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| 8. | | SANTOS, G. B.; CARVALHO, B. C. de; VARGAS, M. W.; MACHADO, F. S.; PIRES, M. de F. A.; CAMPOS, M. M.; VARAGO, F. C.; ALVES, B. R. C. Produção de leite e avaliação automatizada do consumo de matéria seca e de água de vacas diagnosticadas com metrite. In: CONGRESSO INTERNACIONAL DO LEITE, 13., 2015, Porto Alegre. Anais... Juiz de Fora: Embrapa Gado de Leite, 2015. 4 p.| Biblioteca(s): Embrapa Gado de Leite. |
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| 10. | | QUINTAO, C. C. R.; OLIVEIRA, C. S.; FREITAS, C. de; SOUSA, E. D. De; SOUZA, J. F.; ALVES, B. R. C.; CAMARGO, L. S. de A. Desenvolvimento, número de células totais e apoptose em embriões bovinos submetidos à biópsia manual no sétimo dia de fecundação in vitro. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE TECNOLOGIA DE EMBRIÕES, 30., 2016, Foz do Iguaçu. Anais... Foz do Iguaçu: Sociedade Brasileira de Tecnologia de Embriões, 2016. 1| Biblioteca(s): Embrapa Gado de Leite. |
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| 11. | | LEITE, J. de A.; ARCURI, E. F.; RAMOS, A. I. A. P.; LANGE, C. C.; BRITO, M. A. V. P. e; MENDONCA, L. C.; ALVES, B. R. C. Isolamento e avaliação in vitro da eficácia de bacteriófagos líticos específicos para Staphylococcus aureus associados a produtos láticos e mastite bovina. In: CONGRESSO PAN-AMERICANO DO LEITE, 11., 2010, Belo Horizonte. Anais... Belo Horizonte: FAEMG/FEPALE, 2010. 1 CD-ROM.| Biblioteca(s): Embrapa Gado de Leite. |
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| 12. | | LEITE, J. A.; ARCURI, E. F.; RAMOS, A. I. A. P.; SILVA, J. M.; LANGE, C. C.; ALVES, B. R. C. Isolamento e avaliação in vitro da eficácia de bacteriófagos líticos específicos para Staphylococcus aureus causadores de mastite bovina. In: CONGRESSO INTERNACIONAL DO LEITE, 6., 2007, Resende. Anais... Juiz de Fora: Embrapa Gado de Leite, 2007. 1 CD.| Biblioteca(s): Embrapa Gado de Leite. |
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| 13. | | LEITE, J. A.; PEREIRA, H. P.; ALVES, B. R. C.; BORGES, C. A. V.; RAMOS, A. I. A. P.; MARTINS, M. F.; ARCURI, E. F. Lytic bacteriophages as a potential alternative to control Staphylococcus aureus. Pesquisa Agropecuária Brasileira, Brasília, v. 54, e00917, jan.-dez. 2019. Título em português: Bacteriófagos líticos como alternativa potencial para o controle de Staphylococcus aureus.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Unidades Centrais. |
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| 15. | | 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.| Biblioteca(s): Embrapa Gado de Leite. |
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| 16. | | SOUZA, G. N. de; RIBEIRO, A. C. de C. L.; ALVES, B. R. C.; ÁVILA, F. C. de; MENDONÇA, J. F. M. de; PEGORARO, L. M. C.; LILENBAUM, W. Coleta, armazenamento e envio de amostras de material de aborto para diagnóstico de doenças infecciosas em bovinos. Juíz de Fora: Embrapa Gado de Leite, 2017. 6 p. (Embrapa Gado de Leite. Circular Técnica, 116).| Biblioteca(s): Embrapa Clima Temperado; Embrapa Gado de Leite. |
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| 17. | | FONSECA, J. F. da; VERGANI, G. B.; LIMA, M. S. D.; SILVA, K. de M.; MONTEIRO, A. W. U.; RAMOS, A. F.; ALVES, B. R. C.; SOUZA-FABJAN, J. M. G.; OLIVEIRA, M. E. F.; BATISTA, R. I. T. P. Nonsurgical embryo recovery as a feasible tool for supporting embryo biobanks of Locally Adapted Brazilian sheep and goats. Biopreserv Biobank, Nov. 2021. Online ahead of print.| Biblioteca(s): Embrapa Caprinos e Ovinos; Embrapa Recursos Genéticos e Biotecnologia. |
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| 18. | | CARVALHO, B. C.; SERAPIÃO, R. V.; CAMPOS JR. P. H. A.; POLISSENI, J.; PEREIRA, M. M.; ALVES, B. R. C.; OLIVEIRA, A. P.; VIANA, J. H. M.; CAMARGO, L. S. A.; SÁ, W. F. Criotolerância de embriões bovinos produzidos in vitro cultivados em meio CR2 suplementado com knockout tm SR. Acta Scientiae Veterinariae, v. 36, p. s510, 2008. Supl. 2. s510 Edição dos anais da 22ª Reunião Anual da Sociedade Brasileira de Tecnologia de Embriões, Guarujá, 2008.| Biblioteca(s): Embrapa Gado de Leite. |
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| 19. | | COSTA, I. C.; CAMPOS, M. M.; JAYME, C. G.; LIMA, J. A. M.; FONSECA, A. P.; MACHADO, F. S.; PEREIRA, L. G. R.; TOMICH, T. R.; BORGES, A. M.; ALVES, B. R. C. Escores visuais e suas associações com eficiência alimentar e reprodutiva em novilhas F1 Holandês-Gir. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 19., 2017, Juiz de Fora. Anais... Juiz de Fora: Embrapa Gado de Leite, 2017. (Embrapa Gado de Leite. Documentos, 200.). 1 p.| Biblioteca(s): Embrapa Gado de Leite. |
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| 20. | | OLIVEIRA, M. E. F.; ARRAIS, A. M.; VERGANI, G. B.; ESTEVES, S. N.; PEREIRA, V. S. do A.; GARCIA, A. R.; BASTOS, R.; MELO, M. R. B.; ALVES, B. R. C.; FONSECA, J. F. da. Hormonal-induced cervical relaxation during diestrus in ewes: cervical transposing feasibility and use of hCG for rescuing. Disrupted Luteal Function Journal of Applied Animal Welfare Science, 2022. 17 p.| Biblioteca(s): Embrapa Caprinos e Ovinos; Embrapa Pecuária Sudeste. |
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| Registros recuperados : 42 | |
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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. |
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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Circulação/Nível: |
A - 1 |
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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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