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Registro Completo |
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Biblioteca(s): |
Embrapa Amapá. |
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Data corrente: |
30/06/2008 |
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Data da última atualização: |
27/02/2013 |
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Autoria: |
OLIVEIRA, M. Z. A. de; MELLO, S. C. M. de. |
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Título: |
Incidência de fungos toxigenicos em grãos de milho (Zea mays L.) coletados na região de Irece, do estado da Bahia. |
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Ano de publicação: |
1986 |
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Fonte/Imprenta: |
Salvador: EPABA, 1986. |
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Páginas: |
2 p. |
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Série: |
(EPABA. Pesquisa em andamento, 24). |
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Idioma: |
Português |
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Palavras-Chave: |
Bahia; Brasil; Fungos; Graos. |
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Thesagro: |
Milho. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00552nam a2200193 a 4500
001 1351733
005 2013-02-27
008 1986 bl uuuu u0uu1 u #d
100 1 $aOLIVEIRA, M. Z. A. de
245 $aIncidência de fungos toxigenicos em grãos de milho (Zea mays L.) coletados na região de Irece, do estado da Bahia.
260 $aSalvador: EPABA$c1986
300 $a2 p.
490 $a(EPABA. Pesquisa em andamento, 24).
650 $aMilho
653 $aBahia
653 $aBrasil
653 $aFungos
653 $aGraos
700 1 $aMELLO, S. C. M. de
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Registro original: |
Embrapa Amapá (CPAF-AP) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agrossilvipastoril. Para informações adicionais entre em contato com cpamt.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Agrossilvipastoril. |
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Data corrente: |
18/02/2021 |
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Data da última atualização: |
02/12/2021 |
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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: |
KAMCHEN, S. G.; SANTOS, E. F. dos; LOPES, L. B.; VENDRUSCULO, L. G.; CONDOTTA, I. C. F. S. |
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Afiliação: |
SCHEILA GEIELE KAMCHEN, UFMT, Cuiaba-MT; ELTON FERNANDES DOS SANTOS, UFMT, Sinop-MT; LUCIANO BASTOS LOPES, CPAMT; LAURIMAR GONCALVES VENDRUSCULO, CNPTIA; ISABELLA C. F. S. CONDOTTA, University of Illinois. |
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Título: |
Application of depth sensor to estimate body mass and morphometric assessment in Nellore heifers. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Livestock Science, v. 245, 104442, 2021. |
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ISSN: |
1871-1413 |
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DOI: |
https://doi.org/10.1016/j.livsci.2021.104442 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. The potential of an RGB-D sensor as a tool to estimate Nellore heifers´ body mass and morphometric measurements through image analysis was evaluated. An Intel RealSense D435i depth sensor was used to acquire dorsal images of 260 animals aged between 8 and 18 months. Images were acquired from March to December 2019 in six-time points. The best quality images were selected using a multi-layer perceptron neural network (nn = 547). The images were then manually associated with each animal's electronic ear tag. Morphometric measurements were manually acquired using a hipometer and measured on the images using the OpenCV library's graphical interface. These values were acquired in pixels converted to meters. The adjusted linear regression analysis between body mass measured with a scale and estimated body volume presented a high coefficient of determination of R² = 0.97. The mean absolute percentage error was 3.13%, the absolute error was ± 8.85 kg, and the mean squared error was 10.07 kg. The mean absolute error, mean squared error and mean absolute percentage error between manually acquired and digitally acquired morphometric measurements were: 4.23 cm, 5.34 cm, and 18% for chest width (R² = 0.56); 4.4 cm, 5.1 cm, and 13.9% for croup width (R² = 0.86); 6.0 cm, 8.0 cm, and 19.3% for croup length (R² = 0.75); 4.7 cm, 6.6 cm, and 3.8% for croup height (R² = 0.9); and 3.5 cm, 5.1 cm, and 2.9% for withers height (R² = 0.92). This study showed that it is possible to estimate body mass in Nellore heifers using a depth sensor and has good potential for application on morphometric evaluations. MenosAbstract. The potential of an RGB-D sensor as a tool to estimate Nellore heifers´ body mass and morphometric measurements through image analysis was evaluated. An Intel RealSense D435i depth sensor was used to acquire dorsal images of 260 animals aged between 8 and 18 months. Images were acquired from March to December 2019 in six-time points. The best quality images were selected using a multi-layer perceptron neural network (nn = 547). The images were then manually associated with each animal's electronic ear tag. Morphometric measurements were manually acquired using a hipometer and measured on the images using the OpenCV library's graphical interface. These values were acquired in pixels converted to meters. The adjusted linear regression analysis between body mass measured with a scale and estimated body volume presented a high coefficient of determination of R² = 0.97. The mean absolute percentage error was 3.13%, the absolute error was ± 8.85 kg, and the mean squared error was 10.07 kg. The mean absolute error, mean squared error and mean absolute percentage error between manually acquired and digitally acquired morphometric measurements were: 4.23 cm, 5.34 cm, and 18% for chest width (R² = 0.56); 4.4 cm, 5.1 cm, and 13.9% for croup width (R² = 0.86); 6.0 cm, 8.0 cm, and 19.3% for croup length (R² = 0.75); 4.7 cm, 6.6 cm, and 3.8% for croup height (R² = 0.9); and 3.5 cm, 5.1 cm, and 2.9% for withers height (R² = 0.92). This study showed that it is possible to estimate... Mostrar Tudo |
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Palavras-Chave: |
Análise de imagem; Massa corporal; Nellore heifers´ body mass; Novilhas Nelore; Pecuária de precisão; Precision livestock farming; RealSense D435i; Sensor RGB-D. |
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Thesagro: |
Gado; Gado Nelore. |
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Thesaurus NAL: |
Body mass index; Cattle; Heifers; Image analysis. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02682naa a2200361 a 4500
001 2136987
005 2021-12-02
008 2021 bl uuuu u00u1 u #d
022 $a1871-1413
024 7 $ahttps://doi.org/10.1016/j.livsci.2021.104442$2DOI
100 1 $aKAMCHEN, S. G.
245 $aApplication of depth sensor to estimate body mass and morphometric assessment in Nellore heifers.$h[electronic resource]
260 $c2021
520 $aAbstract. The potential of an RGB-D sensor as a tool to estimate Nellore heifers´ body mass and morphometric measurements through image analysis was evaluated. An Intel RealSense D435i depth sensor was used to acquire dorsal images of 260 animals aged between 8 and 18 months. Images were acquired from March to December 2019 in six-time points. The best quality images were selected using a multi-layer perceptron neural network (nn = 547). The images were then manually associated with each animal's electronic ear tag. Morphometric measurements were manually acquired using a hipometer and measured on the images using the OpenCV library's graphical interface. These values were acquired in pixels converted to meters. The adjusted linear regression analysis between body mass measured with a scale and estimated body volume presented a high coefficient of determination of R² = 0.97. The mean absolute percentage error was 3.13%, the absolute error was ± 8.85 kg, and the mean squared error was 10.07 kg. The mean absolute error, mean squared error and mean absolute percentage error between manually acquired and digitally acquired morphometric measurements were: 4.23 cm, 5.34 cm, and 18% for chest width (R² = 0.56); 4.4 cm, 5.1 cm, and 13.9% for croup width (R² = 0.86); 6.0 cm, 8.0 cm, and 19.3% for croup length (R² = 0.75); 4.7 cm, 6.6 cm, and 3.8% for croup height (R² = 0.9); and 3.5 cm, 5.1 cm, and 2.9% for withers height (R² = 0.92). This study showed that it is possible to estimate body mass in Nellore heifers using a depth sensor and has good potential for application on morphometric evaluations.
650 $aBody mass index
650 $aCattle
650 $aHeifers
650 $aImage analysis
650 $aGado
650 $aGado Nelore
653 $aAnálise de imagem
653 $aMassa corporal
653 $aNellore heifers´ body mass
653 $aNovilhas Nelore
653 $aPecuária de precisão
653 $aPrecision livestock farming
653 $aRealSense D435i
653 $aSensor RGB-D
700 1 $aSANTOS, E. F. dos
700 1 $aLOPES, L. B.
700 1 $aVENDRUSCULO, L. G.
700 1 $aCONDOTTA, I. C. F. S
773 $tLivestock Science$gv. 245, 104442, 2021.
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