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Registro Completo |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
19/08/1998 |
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Data da última atualização: |
19/08/1998 |
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Autoria: |
SOUSA, C. N. A. de. |
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Afiliação: |
EMBRAPA-CNPT. |
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Título: |
Classification of Brazilian wheat cultivars for aluminium toxicity in acid soils. |
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Ano de publicação: |
1998 |
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Fonte/Imprenta: |
Plant Breeding, Berlin, v. 117, n. 3, p. 217-221, July 1998. |
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ISSN: |
0179-9541 |
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Idioma: |
Inglês |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00379naa a2200121 a 4500
001 1824955
005 1998-08-19
008 1998 bl uuuu u00u1 u #d
022 $a0179-9541
100 1 $aSOUSA, C. N. A. de
245 $aClassification of Brazilian wheat cultivars for aluminium toxicity in acid soils.
260 $c1998
773 $tPlant Breeding, Berlin$gv. 117, n. 3, p. 217-221, July 1998.
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Registro original: |
Embrapa Trigo (CNPT) |
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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. |
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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Embrapa Agricultura Digital (CNPTIA) |
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