Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil.
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1138334 |
Resumo: | Recent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures. |
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Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil.NDVIRegressão múltiplaPrevisão de SafraRegression analysisAgricultural forecastsRecent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures.THATIANE GOMES ANDRADE, UFPI, Bom Jesus, PI.; ADERSON SOARES DE ANDRADE JUNIOR, CPAMN; MELISSA ODA SOUZA, UESPI, Teresina, PI.; JOSE WELLINGTON BATISTA LOPES, UFPI, Bom Jesus, PI.; PAULO FERNANDO DE MELO JORGE VIEIRA, CPAMN.ANDRADE, T. G.ANDRADE JUNIOR, A. S. deSOUZA, M. O.LOPES, J. W. B.VIEIRA, P. F. de M. J.2021-12-23T15:01:28Z2021-12-23T15:01:28Z2021-12-232022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRevista Caatinga, v. 35, n. 1, p. 105-116, jan./mar. 2022.0100-316X (impresso); 1983-2125 (online)http://www.alice.cnptia.embrapa.br/alice/handle/doc/113833410.1590/1983-21252022v35n111rcenginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2021-12-23T15:01:36Zoai:www.alice.cnptia.embrapa.br:doc/1138334Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542021-12-23T15:01:36Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
title |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
spellingShingle |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. ANDRADE, T. G. NDVI Regressão múltipla Previsão de Safra Regression analysis Agricultural forecasts |
title_short |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
title_full |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
title_fullStr |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
title_full_unstemmed |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
title_sort |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
author |
ANDRADE, T. G. |
author_facet |
ANDRADE, T. G. ANDRADE JUNIOR, A. S. de SOUZA, M. O. LOPES, J. W. B. VIEIRA, P. F. de M. J. |
author_role |
author |
author2 |
ANDRADE JUNIOR, A. S. de SOUZA, M. O. LOPES, J. W. B. VIEIRA, P. F. de M. J. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
THATIANE GOMES ANDRADE, UFPI, Bom Jesus, PI.; ADERSON SOARES DE ANDRADE JUNIOR, CPAMN; MELISSA ODA SOUZA, UESPI, Teresina, PI.; JOSE WELLINGTON BATISTA LOPES, UFPI, Bom Jesus, PI.; PAULO FERNANDO DE MELO JORGE VIEIRA, CPAMN. |
dc.contributor.author.fl_str_mv |
ANDRADE, T. G. ANDRADE JUNIOR, A. S. de SOUZA, M. O. LOPES, J. W. B. VIEIRA, P. F. de M. J. |
dc.subject.por.fl_str_mv |
NDVI Regressão múltipla Previsão de Safra Regression analysis Agricultural forecasts |
topic |
NDVI Regressão múltipla Previsão de Safra Regression analysis Agricultural forecasts |
description |
Recent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-23T15:01:28Z 2021-12-23T15:01:28Z 2021-12-23 2022 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Revista Caatinga, v. 35, n. 1, p. 105-116, jan./mar. 2022. 0100-316X (impresso); 1983-2125 (online) http://www.alice.cnptia.embrapa.br/alice/handle/doc/1138334 10.1590/1983-21252022v35n111rc |
identifier_str_mv |
Revista Caatinga, v. 35, n. 1, p. 105-116, jan./mar. 2022. 0100-316X (impresso); 1983-2125 (online) 10.1590/1983-21252022v35n111rc |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1138334 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1817695625964158976 |