Predicting soybean grain yield using aerial drone images

Abstract

This study aimed to evaluate the ability of vegetation indices (VIs) obtained from unmanned aerial vehicle (UAV) images to estimate soybean grain yield under soil and climate conditions in the Teresina microregion, Piaui state (PI), Brazil. Soybean cv. BRS-8980 was evaluated in stage R5 and submitted to two water regimes (WR) (100 and 50% of crop evapotranspiration - ETc) and two N levels (with and without N supplementation). A randomized block design in a split-plot scheme was used, in which the plots were the water regimes and the subplots N levels, with five replicates. Each plot contained twenty 4.5 m-long rows, spaced 0.5 m apart, with a total area of 45 and 6 m² study area for grain yield evaluations. Twenty VIs obtained from multispectral aerial images were evaluated and correlated with grain yield measurements in the field. Pearson’s correlation, linear regression, and spatial autocorrelation (Global and Local Moran’s I) were used to analyze the performance of the VIs in predicting grain yield. The R2, RMSE and nRMSE indices were used to validate the linear regression models. The prediction model based on EVI-2 exhibited high spatial randomness for all the treatments, and smaller prediction errors of 149.68 and 173.96 kg ha-1 (without and with N supplementation, respectively).