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| Registros recuperados : 7 | |
| 2. |  | RAIMUNDO, S. MENEZES, G. V.; NOFFS, M. da; PISCIOTTA, K. R.; LOREJAN, S.; KOTEZ, L.; SALINAS, S. G. A criação dos conselhos consultivos nas unidades de proteção integral: estudo de caso no Estado de São Paulo. In: CONGRESSO BRASILEIRO DE UNIDADES DE CONSERVAÇÃO, 3., 2002, Fortaleza. Anais. Fortaleza: Rede Nacional Pró-Unidades de Conservação: Fundação O Boticário de Proteção à Natureza, 2002. p. 223-233. Trabalho técnico.| Biblioteca(s): Embrapa Florestas. |
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| 3. | | ANTUNES, A. Z.; ESTON, M. R. de; SANTOS, A. S. R. dos; MENEZES, G. V.; SANTOS, A. M. R. dos. Presença da coruja-listrada Strix hylophila Temminck, 1825 (aves, strigidae) no Parque Estadual Carlos Botelho, São miguel Arcanjo, estado de São Paulo. Revista do Instituto Florestal, São Paulo, v. 18, n. único, p. 167-171, dez. 2006. Nota científica.| Biblioteca(s): Embrapa Florestas. |
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| 4. | | ESTON, M. R. de; MENEZES, G. V.; ANTUNES, A. Z.; SANTOS, A. S. R. dos; SANTOS, A. M. R. dos. Espécie invasora em umidade de conservação: Achatina fulica (Bowdich, 1822) no Parque Estadual Carlos botelho, Sete Barras, SP, Brasil. Revista do Instituto Florestal, São Paulo, v. 18, n. único, p. 173-179, dez. 2006. Nota científica.| Biblioteca(s): Embrapa Florestas. |
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| 5. | | 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. Prediction of Girolando cattle weight by means of body measurements extracted from images. Revista Brasileira de Zootecnia. v. 49, e20190110, 2020.| Biblioteca(s): Embrapa Gado de Corte; Embrapa Pantanal. |
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| 6. | | WEBER, F. de L.; WEBER, V. A. de M.; MENEZES, G. V.; OLIVEIRA JUNIOR, A. da S.; ALVES, D. A.; OLIVEIRA, M. V. M. de; MATSUBARA, E. T.; PISTORI, H.; ABREU, U. G. P. de. Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. Computers and Electronics in Agriculture, v. 175, 105548, p. 1-9, 2020.| Biblioteca(s): Embrapa Pantanal. |
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| 7. | | WEBER, V. A. M.; WEBER, F. de L.; OLIVEIRA, A. da S.; ASTOLFI, G.; MENEZES, G. V.; PORTO, J. V. de A.; REZENDE, F. P. C.; MORAES, P. H. de; MATSUBARA, E. T.; MATEUS, R. G.; ARAÚJO, T. L. A. C. de; SILVA, L. O. C. da; QUEIROZ, E. Q, A. de; ABREU, U. G. P. de; GOMES, R. da C.; PISTORI, H. Cattle weight estimation using active contour models and regression trees Bagging . Computers and Electronics in Agriculture, v.179, 105804, p. 1-12, dec, 2020.| Biblioteca(s): Embrapa Pantanal. |
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| Registros recuperados : 7 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pantanal. |
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Data corrente: |
25/03/2020 |
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Data da última atualização: |
20/04/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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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. |
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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. |
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Título: |
Prediction of Girolando cattle weight by means of body measurements extracted from images. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Revista Brasileira de Zootecnia. v. 49, e20190110, 2020. |
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Idioma: |
Inglês
Português |
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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 |
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Palavras-Chave: |
Livestock precision; Machine learning; Mass estimation. |
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Thesagro: |
Gado de Corte; Gado Gir; Morfologia Animal; Peso. |
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Thesaurus NAL: |
Beef cattle; Body weight; Cattle; Computer vision; Gir (cattle breed); Livestock production. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
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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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Embrapa Gado de Corte (CNPGC) |
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