| |
|
|
| Registros recuperados : 14 | |
| 5. |  | HAMADA, E.; GHINI, R.; ROSSI, P.; PEDRO JÚNIOR, M. J.; FERNANDES, J. L. Climatic risk of grape downy mildew (Plasmopara viticola) for the State of São Paulo, Brazil. Scientia Agricola, Piracicaba, v. 65, n. esp., p. 60-64, 2008.| Biblioteca(s): Embrapa Meio Ambiente. |
|    |
| 7. | | HAMADA, E.; GHINI, R.; FERNANDES, J. L.; PEDRO JÚNIOR, M. J.; ROSSI, P. Spatial and temporal variability of leaf wetness duration in the State of São Paulo, Brazil. Scientia Agricola, Piracicaba, v. 65, n. esp., p. 26-31, 2008.| Biblioteca(s): Embrapa Meio Ambiente. |
| |
| Registros recuperados : 14 | |
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
14/09/2017 |
|
Data da última atualização: |
07/01/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
FERNANDES, J. L.; EBECKEN, N. F. F.; ESQUERDO, J. C. D. M. |
|
Afiliação: |
JEFERSON LOBATO FERNANDES, COPPE/UFRJ; NELSON FRANCISCO FAVILLA EBECKEN, COPPE/UFRJ; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA. |
|
Título: |
Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
International Journal of Remote Sensing, Basingstoke, v. 38, n 16, p. 4631-4644, 2017. |
|
DOI: |
http://dx.doi.org/10.1080/01431161.2017.1325531 |
|
Idioma: |
Inglês |
|
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 |
|
Palavras-Chave: |
Artificial neural networks; Redes neurais; Séries temporais. |
|
Thesagro: |
Cana de açúcar. |
|
Thesaurus NAL: |
Neural networks; Sugarcane; Time series analysis. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
