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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: |
14/09/2017 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERNANDES, J. L.; EBECKEN, N. F. F.; ESQUERDO, J. C. D. M. |
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Afiliação: |
JEFERSON LOBATO FERNANDES, COPPE/UFRJ; NELSON FRANCISCO FAVILLA EBECKEN, COPPE/UFRJ; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA. |
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Título: |
Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
International Journal of Remote Sensing, Basingstoke, v. 38, n 16, p. 4631-4644, 2017. |
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DOI: |
http://dx.doi.org/10.1080/01431161.2017.1325531 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT. The objective of this study is to predict the sugarcane yield in São Paulo State, Brazil, using metrics derived from normalized difference vegetation index (NDVI) time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and an ensemble model of artificial neural networks (ANNs). Sixty municipalities were selected and spectral metrics were extracted from the NDVI time series for each municipality from 2003 to 2012. A neural network wrapper with sequential backward elimination was applied to remove irrelevant and/or redundant features from the initial data set, reducing over-fitting and improving the prediction performance. Afterwards the sugarcane yield was predicted using a stacking ensemble model with ANN. At the predicted yield, the relative root mean square error (RRMSE) was 6.8% and the coefficient of determination (R2) was 0.61. The last three months were removed from the initial time-series data set to forecast the final sugarcane yield, and the process was repeated. The feature selection (FS) improved again the prediction performance and Stacking improved the FS results: RRMSE increased to 8% and R2 to 0.43. The yield was also estimated for the entire State, based on the average of the 60 selected municipalities, which were compared to the official data surveys. The Stacking method was able to estimate the sugarcane yield for São Paulo State with a smaller RMSE than the official data surveys, anticipating the crop forecast by three months before the harvest. MenosABSTRACT. The objective of this study is to predict the sugarcane yield in São Paulo State, Brazil, using metrics derived from normalized difference vegetation index (NDVI) time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and an ensemble model of artificial neural networks (ANNs). Sixty municipalities were selected and spectral metrics were extracted from the NDVI time series for each municipality from 2003 to 2012. A neural network wrapper with sequential backward elimination was applied to remove irrelevant and/or redundant features from the initial data set, reducing over-fitting and improving the prediction performance. Afterwards the sugarcane yield was predicted using a stacking ensemble model with ANN. At the predicted yield, the relative root mean square error (RRMSE) was 6.8% and the coefficient of determination (R2) was 0.61. The last three months were removed from the initial time-series data set to forecast the final sugarcane yield, and the process was repeated. The feature selection (FS) improved again the prediction performance and Stacking improved the FS results: RRMSE increased to 8% and R2 to 0.43. The yield was also estimated for the entire State, based on the average of the 60 selected municipalities, which were compared to the official data surveys. The Stacking method was able to estimate the sugarcane yield for São Paulo State with a smaller RMSE than the official data surveys, anticipating the crop forecast by three m... Mostrar Tudo |
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Palavras-Chave: |
Artificial neural networks; Redes neurais; Séries temporais. |
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Thesagro: |
Cana de açúcar. |
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Thesaurus Nal: |
Neural networks; Sugarcane; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02327naa a2200241 a 4500
001 2075572
005 2020-01-07
008 2017 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1080/01431161.2017.1325531$2DOI
100 1 $aFERNANDES, J. L.
245 $aSugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble.$h[electronic resource]
260 $c2017
520 $aABSTRACT. The objective of this study is to predict the sugarcane yield in São Paulo State, Brazil, using metrics derived from normalized difference vegetation index (NDVI) time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and an ensemble model of artificial neural networks (ANNs). Sixty municipalities were selected and spectral metrics were extracted from the NDVI time series for each municipality from 2003 to 2012. A neural network wrapper with sequential backward elimination was applied to remove irrelevant and/or redundant features from the initial data set, reducing over-fitting and improving the prediction performance. Afterwards the sugarcane yield was predicted using a stacking ensemble model with ANN. At the predicted yield, the relative root mean square error (RRMSE) was 6.8% and the coefficient of determination (R2) was 0.61. The last three months were removed from the initial time-series data set to forecast the final sugarcane yield, and the process was repeated. The feature selection (FS) improved again the prediction performance and Stacking improved the FS results: RRMSE increased to 8% and R2 to 0.43. The yield was also estimated for the entire State, based on the average of the 60 selected municipalities, which were compared to the official data surveys. The Stacking method was able to estimate the sugarcane yield for São Paulo State with a smaller RMSE than the official data surveys, anticipating the crop forecast by three months before the harvest.
650 $aNeural networks
650 $aSugarcane
650 $aTime series analysis
650 $aCana de açúcar
653 $aArtificial neural networks
653 $aRedes neurais
653 $aSéries temporais
700 1 $aEBECKEN, N. F. F.
700 1 $aESQUERDO, J. C. D. M.
773 $tInternational Journal of Remote Sensing, Basingstoke$gv. 38, n 16, p. 4631-4644, 2017.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
17/05/2022 |
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Data da última atualização: |
17/05/2022 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
OLIVEIRA JUNIOR, M. C. M. de; POCCARD-CHAPUIS, R. J. M.; HOMMA, A. K. O.; VENTURIERI, A.; ALMEIDA, C. A. de; THÉRY, H. |
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Afiliação: |
MOISES CORDEIRO MOURAO DE OLIVEIRA, CPATU; RENÉ JEAN MARIE POCCARD-CHAPUIS, Centre de Coopération Internationale en Recherche Agronomique pour le Développement; ALFREDO KINGO OYAMA HOMMA, CPATU; ADRIANO VENTURIERI, CPATU; CLÁUDIO APARECIDO DE ALMEIDA, INPE; HERVÉ THÉRY, Centre National de la Recherche Scientifique / USP. |
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Título: |
Dinâmica do uso e cobertura do solo no estado do Pará: pastagens na década de 2000, segundo o TerraClass. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: HOMMA, A. K. O. (ed.). Sinergias de mudança da agricultura amazônica: conflitos e oportunidades. Brasília, DF: Embrapa, 2022. |
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Páginas: |
p. 128-154. |
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Idioma: |
Português |
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Thesagro: |
Cobertura do Solo; Pastagem; Uso da Terra. |
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Thesaurus NAL: |
Amazonia. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143098/1/LV-Sinergias-130-156.pdf
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Marc: |
LEADER 00792naa a2200229 a 4500
001 2143098
005 2022-05-17
008 2022 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA JUNIOR, M. C. M. de
245 $aDinâmica do uso e cobertura do solo no estado do Pará$bpastagens na década de 2000, segundo o TerraClass.$h[electronic resource]
260 $c2022
300 $ap. 128-154.
650 $aAmazonia
650 $aCobertura do Solo
650 $aPastagem
650 $aUso da Terra
700 1 $aPOCCARD-CHAPUIS, R. J. M.
700 1 $aHOMMA, A. K. O.
700 1 $aVENTURIERI, A.
700 1 $aALMEIDA, C. A. de
700 1 $aTHÉRY, H.
773 $tIn: HOMMA, A. K. O. (ed.). Sinergias de mudança da agricultura amazônica: conflitos e oportunidades. Brasília, DF: Embrapa, 2022.
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