ENHANCED WATER MONITORING AND CORN YIELD PREDICTION USING RPA-DERIVED IMAGERY

ABSTRACT Traditional methods for assessing crop water status have limited practicality for field applications. Conversely, remote sensing via remotely piloted aircraft (RPA) offers a promising alternative, though its effectiveness requires validation in specific studies. This study utilized RPA-derived imagery to support strategies for monitoring water deficit (WD) in corn crops and enabling yield prediction. The AG 1051 corn genotype underwent deficit irrigation levels (20, 40, 60, 80, and 100% of crop evapotranspiration—ETc) at different phenological stages: initial (E1), vegetative growth (E2), flowering (E3), and physiological maturity (E4) in Iguatu, Ceará, Brazil. We measured ten vegetation indices (VIs) and ear and biomass yield. Pearson's correlation analysis revealed that, during E2, the normalized difference vegetation index (NDVI) (r = 0.98) and green leaf index (GLI) (r = 0.99) were the most reliable for distinguishing water stress levels. These indices were also effective in predicting yield in E2 through regression analysis. The findings demonstrate that vegetation indices derived from RPA imagery provide a robust method for assessing water conditions and forecasting yield.