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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
14/12/2021 |
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Data da última atualização: |
14/12/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
ALMEIDA, H. S. L.; REIS, A. A. dos; WERNER, J. P. S.; ANTUNES, J. F. G.; ZHONG, L.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. |
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Afiliação: |
HENRIQUE S. L. ALMEIDA, UNICAMP; ALINY APARECIDA DOS REIS, UNICAMP; JOÃO PAULO SAMPAIO WERNER, UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LIHENG ZHONG, Ant Group, World Financial Center, Beijing; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, UNICAMP; PAULO S. G. MAGALHÃES, UNICAMP. |
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Título: |
Deep neural networks for mapping integrated crop-livestock systems using PlanetScope time series. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE, 2021. |
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Páginas: |
p. 4224-4227. |
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ISBN: |
978-1-6654-0369-6 |
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DOI: |
10.1109/IGARSS47720.2021.9554500 |
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Idioma: |
Inglês |
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Notas: |
IGARSS 2021. Paper WE2.MM-8.3. |
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Conteúdo: |
Abstract: Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area. |
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Palavras-Chave: |
Aprendizado profundo; Convolutional Neural Networks; Deep learning; EVI; Nano-Satellites; Nanossatélites; NDVI; Redes neurais; Redes neurais convolucionais; Redes neurais profundas; Séries temporais; Sistemas de integração lavoura-pecuária. |
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Thesaurus Nal: |
Neural networks; Time series analysis. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02288nam a2200433 a 4500
001 2137800
005 2021-12-14
008 2021 bl uuuu u00u1 u #d
020 $a978-1-6654-0369-6
024 7 $a10.1109/IGARSS47720.2021.9554500$2DOI
100 1 $aALMEIDA, H. S. L.
245 $aDeep neural networks for mapping integrated crop-livestock systems using PlanetScope time series.$h[electronic resource]
260 $aIEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE$c2021
300 $ap. 4224-4227.
500 $aIGARSS 2021. Paper WE2.MM-8.3.
520 $aAbstract: Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area.
650 $aNeural networks
650 $aTime series analysis
653 $aAprendizado profundo
653 $aConvolutional Neural Networks
653 $aDeep learning
653 $aEVI
653 $aNano-Satellites
653 $aNanossatélites
653 $aNDVI
653 $aRedes neurais
653 $aRedes neurais convolucionais
653 $aRedes neurais profundas
653 $aSéries temporais
653 $aSistemas de integração lavoura-pecuária
700 1 $aREIS, A. A. dos
700 1 $aWERNER, J. P. S.
700 1 $aANTUNES, J. F. G.
700 1 $aZHONG, L.
700 1 $aFIGUEIREDO, G. K. D. A.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aMAGALHÃES, P. S. G.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
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Origem |
Tipo/Formato |
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Cutter |
Registro |
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URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
27/01/2015 |
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Data da última atualização: |
05/02/2024 |
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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: |
MACEDO, A. A.; BITTAR, J. F. F.; BASSI, P. B.; RONDA, J. B.; BITTAR, E. R.; PANETTO, J. C. do C.; ARAUJO, M. S. S.; SANTOS, R. L.; MARTINS-FILHO, O. A. |
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Afiliação: |
AURICÉLIO A MACEDO, UFMG; JOELY F. F. BITTAR, Universidade de Uberaba; Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz; PAULA B BASSI, Universidade de Uberaba; JULIANO B. RONDA, Universidade de Uberaba; EUSTÁQUIO R. BITTAR, Universidade de Uberaba; JOAO CLAUDIO DO CARMO PANETTO, CNPGL; MÁRCIO S. S. ARAUJO, Universidade de Uberaba; Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz; RENATO L. SANTOS, UFMG; OLINDO A. MARTINS-FILHO, Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz. |
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Título: |
Influence of endogamy and mitochondrial DNA on immunological parameters in cattle. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
BMC Veterinary Research, v. 10, n. 79, 2014. |
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Idioma: |
Inglês |
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Conteúdo: |
Endogamy increases the risk of manifestation of deleterious recessive genes. Mitochondrial DNA allows the separation of American Zebu (Bos indicus and Bos taurus) and evaluate the effect of mitochondrial DNA on productive traits of cattle. However, the effect of endogamy and mitochondrial DNA (mtDNA) on the immune system remains unclear. The aim of this study was to evaluate the association between endogamy, mtDNA and immune parameters. The results demonstrated for the first time that endogamy influences the immune system of cattle. |
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Palavras-Chave: |
Endogamy; MtDNA. |
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Thesaurus NAL: |
cattle; flow cytometry. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/116544/1/Cnpgl-2014-BMC-Vet-Research-Influence-of-endogamy.pdf
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Marc: |
LEADER 01270naa a2200265 a 4500
001 2006793
005 2024-02-05
008 2014 bl uuuu u00u1 u #d
100 1 $aMACEDO, A. A.
245 $aInfluence of endogamy and mitochondrial DNA on immunological parameters in cattle.$h[electronic resource]
260 $c2014
520 $aEndogamy increases the risk of manifestation of deleterious recessive genes. Mitochondrial DNA allows the separation of American Zebu (Bos indicus and Bos taurus) and evaluate the effect of mitochondrial DNA on productive traits of cattle. However, the effect of endogamy and mitochondrial DNA (mtDNA) on the immune system remains unclear. The aim of this study was to evaluate the association between endogamy, mtDNA and immune parameters. The results demonstrated for the first time that endogamy influences the immune system of cattle.
650 $acattle
650 $aflow cytometry
653 $aEndogamy
653 $aMtDNA
700 1 $aBITTAR, J. F. F.
700 1 $aBASSI, P. B.
700 1 $aRONDA, J. B.
700 1 $aBITTAR, E. R.
700 1 $aPANETTO, J. C. do C.
700 1 $aARAUJO, M. S. S.
700 1 $aSANTOS, R. L.
700 1 $aMARTINS-FILHO, O. A.
773 $tBMC Veterinary Research$gv. 10, n. 79, 2014.
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Embrapa Gado de Leite (CNPGL) |
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