Predicting In-Season Corn Grain Yield Using Optical Sensors

In-season sensing can account for field variability and improve nitrogen (N) management; however, opportunities exist refinement. The purpose of this study was to compare different sensors vegetation indices (VIs) (normalized difference index (NDVI); normalized red edge (NDRE); Simplified Canopy Chlorophyll Content Index (SCCCI)) at various corn stages predict in-season yield potential. Additionally, methods prediction were evaluated where the final regressed against raw or % reflectance VIs, relative estimates (INSEY, VI divided by growing degree days). Field experiments eight-site years established in Mississippi. Crop data collected using an at-leaf SPAD sensor, two proximal sensors: GreenSeeker Circle, a small unmanned aerial system (sUAS) equipped with MicaSense sensor. Overall, measurements superior grain prediction. best predicted VT-R1 (R2 = 0.78–0.83), Circle VT 0.57 0.49), V10 0.52). When VIs compared, SCCCI 0.40–0.49) outperformed other terms achieved MicaSense-derived growth stages.