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Registro Completo |
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Biblioteca(s): |
Embrapa Meio-Norte. |
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Data corrente: |
23/12/2021 |
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Data da última atualização: |
13/02/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ANDRADE, T. G.; ANDRADE JUNIOR, A. S. de; SOUZA, M. O.; LOPES, J. W. B.; VIEIRA, P. F. de M. J. |
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Afiliação: |
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. |
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Título: |
Soybean yield prediction using remote sensing in Southwestern Piauí State, Brazil. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Revista Caatinga, v. 35, n. 1, p. 105-116, jan./mar. 2022. |
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ISSN: |
0100-316X (impresso); 1983-2125 (online) |
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DOI: |
10.1590/1983-21252022v35n111rc |
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Idioma: |
Inglês |
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Conteúdo: |
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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Palavras-Chave: |
NDVI; Regressão múltipla. |
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Thesagro: |
Previsão de Safra. |
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Thesaurus Nal: |
Agricultural forecasts; Regression analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/229625/1/SoybeanYieldPredictionRemoteSensing.pdf
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Marc: |
LEADER 02063naa a2200253 a 4500
001 2138334
005 2023-02-13
008 2022 bl uuuu u00u1 u #d
022 $a0100-316X (impresso); 1983-2125 (online)
024 7 $a10.1590/1983-21252022v35n111rc$2DOI
100 1 $aANDRADE, T. G.
245 $aSoybean yield prediction using remote sensing in Southwestern Piauí State, Brazil.$h[electronic resource]
260 $c2022
520 $aRecent 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.
650 $aAgricultural forecasts
650 $aRegression analysis
650 $aPrevisão de Safra
653 $aNDVI
653 $aRegressão múltipla
700 1 $aANDRADE JUNIOR, A. S. de
700 1 $aSOUZA, M. O.
700 1 $aLOPES, J. W. B.
700 1 $aVIEIRA, P. F. de M. J.
773 $tRevista Caatinga$gv. 35, n. 1, p. 105-116, jan./mar. 2022.
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