Affiliation:
1. Universidade Federal do Piauí, Brazil
2. Embrapa Meio-Norte, Brazil
3. Universidade Estadual do Piauí, Brazil
Abstract
ABSTRACT 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.
Subject
General Agricultural and Biological Sciences
Reference29 articles.
1. Estimating crop coefficients from fraction of ground cover and height;ALLEN R. G;Irrigation Science,2009
2. Atlas Climatológico do Estado do Piauí;ANDRADE JUNIOR A. S,2004
3. Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs;ATZBERGER C;Remote Sensing,2013
4. New Look at Statistical Model Identification;AKAIKE H. A;Transactions on Automatic Control,1974
5. Crescimento de plantas de soja em função da redução da densidade de semeadura e sua relação com a produtividade;BALBINOT JUNIOR A. A,2018
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