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| Registros recuperados : 23 | |
| 2. |  | AFONSO, R. F.; FONSECA, A. P.; PELLEGRIN, A. O. O javali na mídia nacional (2017-2023): temas, impactos e stakeholders. In: EVENTO DE INICIAÇÃO CIENTÍFICA DO PANTANAL, 10., 2023, Corumbá. Resumos... Brasília, DF: Embrapa, 2023. p. 13. (Embrapa Pantanal. Eventos técnicos & científicos, 1). Evinci 2023.| Biblioteca(s): Embrapa Pantanal. |
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| 7. | | PINTO, J. C.; FONSECA, A, P. M.; TOMAS, W. M.; PELLEGRIN, A. O. Identificação de rastros de mamíferos: despertando o espírito investigador sobre a natureza. In: EVENTO DE INICIAÇÃO CIENTÍFICA DO PANTANAL, 8., 2020, Corumbá. Resumos... Brasília, DF: Embrapa, 2020. p. 13.| Biblioteca(s): Embrapa Pantanal. |
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| 9. | | FONSECA, A. P. M.; ROSA, M. O.; MOREIRA, T. de A.; PEREIRA, L. E.; JULIANO, R. S.; PELLEGRIN, A. O. Mapeamento participativo da ocorrência de javali asselvajado na região da Grande Dourados: resultados preliminares. In: EVENTO DE INICIAÇÃO CIENTÍFICA DO PANTANAL, 7., 2019, Corumbá. Resumos... Brasília, DF: Embrapa, 2019. p. 15.| Biblioteca(s): Embrapa Pantanal. |
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| 10. | | FONSECA, A. P. da; SILVA, E. C. da; PEREIRA, M. B.; OLIVEIRA, R. P. de; DORNELLES, A. L. C. Estabilidade fenotípica de genótipos de morangueiro submetidos a número variável de subcultivos in vitro. Ciência Rural, Santa Maria, v. 43, n. 8, p.1345-1350, ago, 2013.| Biblioteca(s): Embrapa Clima Temperado. |
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| 13. | | PAZ, E. S. L. da; FONSECA, A. P. G. da; FREITAS, L. R.; GUARANÁ, C. F. R.; PAZ JÚNIOR, F. B. da. Isolamento e avaliação da atividade fenoloxidase de Basidiomycetes coletados em área de Mata Atlântica - PE. Cientec, Revista de Ciência, Tecnologia e Humanidades do IFPE, v. 2, n. 1, p. 37-51, 2010. Separata de Cientec, Revista de Ciência, Tecnologia e Humanidades do IFPE, v. 2, n. 1, 2010.| Biblioteca(s): Embrapa Solos / UEP-Recife. |
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| 14. | | NASSU, R. T.; TULLIO, R. R.; CRUZ, G. M. da; VERRUMA-BERNARDI, M. R.; BARIONI JUNIOR, W.; FONSECA, A. P. C.; GOMES, T. A. N. Análise sensorial e qualidade da carne maturada de animais cruzados Angus x Nelore e Senepol x Nelore. In: SIMPÓSIO LATINO AMERICANO DE CIÊNCIA DE ALIMENTOS, 2009, Campinas. Ciência de alimentos no mundo globalizado: Novos desfios, novas perspectivas: resumos. Campinas: ACTA: UNICAMP: FEA, 2009. 1 CD-ROM| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 15. | | ANGELO, P. C. da S.; ATROCH, A. L.; NASCIMENTO FILHO, F. J. do; SOUSA, N. R.; MENDONÇA, W. da S.; FONSECA, A. P. A. da. Padrões de florescimento de clones de guaranazeiro. In: SEMINÁRIO SOBRE PESQUISAS COM O GUARANAZEIRO NA AMAZÔNIA, 1., 2005, Manaus. Anais... Manaus: Embrapa Amazônia Ocidental, 2005. p. 25-29. 1 CD-ROM.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| 17. | | NASSU, R. T.; TULLIO, R. R.; NOGUEIRA, A. R. de A.; CRUZ, G. M. da; PICCHI, C. M. C.; GOMES, T. A. N.; FONSECA, A. P. de C. Composição mineral de carne ovina proveniente de três grupos genéticos. In: CONGRESSO BRASILEIRO DE CIÊNCIA E TECNOLOGIA DE ALIMENTOS, 21.; SEMINÁRIO LATINO AMERICANO E DO CARIBE DE CIÊNCIA E TECNOLOGIA DE ALIMENTOS, 15., 2008, Belo Horizonte. Anais... Belo Horizonte: sbCTA, 2008.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 18. | | NASSU, R. T.; VERRUMA-BERNARDI, M. R.; TULLIO, R. R.; CRUZ, G. M. da; BARIONI JUNIOR, W. B.; FONSECA, A. P. C.; GOMES, T. A. N. Desenvolvimento de terminologia descritiva e análise sensorial de carne bovina maturada proveniente de animais cruzados Angus X Nelore e Senepol X Nelore. In: CONGRESSO BRASILEIRO DE CIÊNCIA E TECNOLOGIA DE CARNES, 5., 2009, Campinas, SP. Anais... Campinas: ITAL: CTC, 2009.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 19. | | SANTOS, A. L. M.; SACRAMENTO, J. P.; LEAO, A. E.; FONSECA, A. P.; CAMPOS, M. M.; MACHADO, F. S.; PEREIRA, L. G. R.; TOMICH, T. R. Efeitos da vacinação contra febre aftosa sobre o comportamento e produção de vacas leiteiras. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 24., 2019, Juiz de Fora. Anais... Juiz de Fora: Embrapa Gado de Leite, 2019. 4 p. Editor Técnico: Leônidas Paixão Passos, Embrapa Gado de Leite.| Biblioteca(s): Embrapa Gado de Leite. |
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| 20. | | 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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| Registros recuperados : 23 | |
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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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