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Biblioteca(s): |
Embrapa Meio-Norte. |
Data corrente: |
31/01/2012 |
Data da última atualização: |
03/06/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
ALVES, A. A.; SALES, R. de O.; NEIVA, J. N. M.; MEDEIROS, A. N.; BRAGA, A. P.; AZEVEDO, D. M. M. R.; SILVA, L. R. F. da. |
Afiliação: |
ARNAUD AZEVÊDO ALVES, UFPI; RONALDO DE OLIVEIRA SALES, UFC; JOSÉ NEUMAN MIRANDA NEIVA, UFT; ARIOSVALDO NUNES MEDEIROS, UFPB; ALEXANDRE PAULA BRAGA, UFERSA; DANIELLE MARIA MACHADO R AZEVEDO, CPAMN; LÍLIA RAQUEL FÉ DA SILVA, UFPI. |
Título: |
Metabolismo de compostos nitrogenados em ovinos alimentados com dietas contendo vagens de faveira. |
Ano de publicação: |
2011 |
Fonte/Imprenta: |
Revista Brasileira de Saúde e Produção Animal, Salvador, v. 12, n. 4, p. 1051-1066, out./dez 2011. |
Série: |
1519-9940 |
Idioma: |
Português |
Palavras-Chave: |
Balanço de nitrogênio; Retenção de nitrogênio. |
Thesagro: |
Valor Nutritivo. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/53212/1/DanielleAzevedo.pdf
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Marc: |
LEADER 00742naa a2200229 a 4500 001 1913982 005 2022-06-03 008 2011 bl uuuu u00u1 u #d 100 1 $aALVES, A. A. 245 $aMetabolismo de compostos nitrogenados em ovinos alimentados com dietas contendo vagens de faveira. 260 $c2011 490 $a1519-9940 650 $aValor Nutritivo 653 $aBalanço de nitrogênio 653 $aRetenção de nitrogênio 700 1 $aSALES, R. de O. 700 1 $aNEIVA, J. N. M. 700 1 $aMEDEIROS, A. N. 700 1 $aBRAGA, A. P. 700 1 $aAZEVEDO, D. M. M. R. 700 1 $aSILVA, L. R. F. da 773 $tRevista Brasileira de Saúde e Produção Animal, Salvador$gv. 12, n. 4, p. 1051-1066, out./dez 2011.
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Embrapa Meio-Norte (CPAMN) |
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Registro Completo
Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pantanal. |
Data corrente: |
25/03/2020 |
Data da última atualização: |
20/04/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
WEBER, V. A. de M.; WEBER, F. de L.; GOMES, R. da C.; OLIVEIRA JUNIOR, A. da S.; MENEZES, G. V.; ABREU, U. G. P. de; BELETE, N. A. de S.; PISTORI, H. |
Afiliação: |
Vanessa Aparecida de Moraes Weber, Universidade Católica Dom Bosco - UCDB; Fabricio de Lima Weber, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; RODRIGO DA COSTA GOMES, CNPGC; Adair da Silva Oliveira Junior, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; Geazy Vilharva Menezes, Universidade Federal de Mato Grosso do Sul - UFMS/Faculdade de Computação; URBANO GOMES PINTO DE ABREU, CPAP; Nícolas Alessandro de Souza Belete, Universidade Católica Dom Bosco - UCDB; Hemerson Pistori, Universidade Católica Dom Bosco - UCDB. |
Título: |
Prediction of Girolando cattle weight by means of body measurements extracted from images. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Revista Brasileira de Zootecnia. v. 49, e20190110, 2020. |
Idioma: |
Inglês Português |
Conteúdo: |
The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images. MenosThe objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted fro... Mostrar Tudo |
Palavras-Chave: |
Livestock precision; Machine learning; Mass estimation. |
Thesagro: |
Gado de Corte; Gado Gir; Morfologia Animal; Peso. |
Thesaurus NAL: |
Beef cattle; Body weight; Cattle; Computer vision; Gir (cattle breed); Livestock production. |
Categoria do assunto: |
-- L Ciência Animal e Produtos de Origem Animal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/212007/1/Prediction-of-girolando-cattle.pdf
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Marc: |
LEADER 02545naa a2200361 a 4500 001 2121364 005 2020-04-20 008 2020 bl uuuu u00u1 u #d 100 1 $aWEBER, V. A. de M. 245 $aPrediction of Girolando cattle weight by means of body measurements extracted from images.$h[electronic resource] 260 $c2020 520 $aThe objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HWI (cm) + 0.01929 * DAI (cm2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images. 650 $aBeef cattle 650 $aBody weight 650 $aCattle 650 $aComputer vision 650 $aGir (cattle breed) 650 $aLivestock production 650 $aGado de Corte 650 $aGado Gir 650 $aMorfologia Animal 650 $aPeso 653 $aLivestock precision 653 $aMachine learning 653 $aMass estimation 700 1 $aWEBER, F. de L. 700 1 $aGOMES, R. da C. 700 1 $aOLIVEIRA JUNIOR, A. da S. 700 1 $aMENEZES, G. V. 700 1 $aABREU, U. G. P. de 700 1 $aBELETE, N. A. de S. 700 1 $aPISTORI, H. 773 $tRevista Brasileira de Zootecnia.$gv. 49, e20190110, 2020.
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