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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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Biblioteca |
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Origem |
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Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
22/01/2008 |
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Data da última atualização: |
16/03/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
FARIA, S. A. S. C.; CARVALHO, O. T.; YOSHIME, L. T.; KOAKUZU, S. N.; BASSINELLO, P. Z.; FÁVARO, D. I. T.; PENTEADO, M. D. V. C. |
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Afiliação: |
SIMONE A. S. C. FARIA, Faculdade de Ciências Farmacêuticas, USP; OTILIA T. CARVALHO, Faculdade de Ciências Farmacêuticas, USP; LUCIANA T. YOSHIME, Faculdade de Ciências Farmacêuticas, USP; SELMA NAKAMOTO KOAKUZU, CNPAF; PRISCILA ZACZUK BASSINELLO, CNPAF; DÉBORA I. T. FÁVARO, Instituto de Pesquisas Energéticas e Nucleares, CNEN; MARILENE D. V. C. PENTEADO, Faculdade de Ciências Farmacêuticas, USP. |
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Título: |
Caracterização de minerais em farelo de arroz. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 5.; REUNIÃO DA CULTURA DO ARROZ IRRIGADO, 27., 2007, Pelotas. Anais... Pelotas: Embrapa Clima Temperado, 2007. |
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Volume: |
v.2. |
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Páginas: |
p. 522-524. |
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Idioma: |
Português |
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Conteúdo: |
O objetivo do presente trabalho foi avaliar a variação da composição de minerais nas amostras de farelo de arroz fornecidas pela EMBRAPA, cultivadas em sistema irrigado e sequeiro e submetidas a diferentes processamentos e técnicas de estabilização, tendo em vista que o farelo pode ser utilizado como alimento e/ou ingrediente com propriedades nutricionais. |
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Palavras-Chave: |
Composição de mineral; Farelo de arroz; Nutrição alimentar. |
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Thesagro: |
Arroz; Nutriente. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/215938/1/cbar-2007-p522.pdf
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Marc: |
LEADER 01209nam a2200265 a 4500
001 1215938
005 2023-03-16
008 2007 bl uuuu u00u1 u #d
100 1 $aFARIA, S. A. S. C.
245 $aCaracterização de minerais em farelo de arroz.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 5.; REUNIÃO DA CULTURA DO ARROZ IRRIGADO, 27., 2007, Pelotas. Anais... Pelotas: Embrapa Clima Temperado$c2007
300 $ap. 522-524. v.2.
490 $vv.2.
520 $aO objetivo do presente trabalho foi avaliar a variação da composição de minerais nas amostras de farelo de arroz fornecidas pela EMBRAPA, cultivadas em sistema irrigado e sequeiro e submetidas a diferentes processamentos e técnicas de estabilização, tendo em vista que o farelo pode ser utilizado como alimento e/ou ingrediente com propriedades nutricionais.
650 $aArroz
650 $aNutriente
653 $aComposição de mineral
653 $aFarelo de arroz
653 $aNutrição alimentar
700 1 $aCARVALHO, O. T.
700 1 $aYOSHIME, L. T.
700 1 $aKOAKUZU, S. N.
700 1 $aBASSINELLO, P. Z.
700 1 $aFÁVARO, D. I. T.
700 1 $aPENTEADO, M. D. V. C.
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Embrapa Arroz e Feijão (CNPAF) |
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