Predicting In-Season Corn Grain Yield Using Optical Sensors
2022
Camden Oglesby | Amelia A. A. Fox | Gurbir Singh | Jagmandeep Dhillon
In-season sensing can account for field variability and improve nitrogen (N) management: however, opportunities exist for refinement. The purpose of this study was to compare different sensors and vegetation indices (VIs) (normalized difference vegetation index (NDVI): normalized difference red edge (NDRE): Simplified Canopy Chlorophyll Content Index (SCCCI)) at various corn stages to predict in-season yield potential. Additionally, different methods of yield prediction were evaluated where the final yield was regressed against raw or % reflectance VIs, relative VIs, and in-season yield estimates (INSEY, VI divided by growing degree days). Field experiments at eight-site years were established in Mississippi. Crop reflectance data were collected using an at-leaf SPAD sensor, two proximal sensors: GreenSeeker and Crop Circle, and a small unmanned aerial system (sUAS) equipped with a MicaSense sensor. Overall, relative VI measurements were superior for grain yield prediction. MicaSense best predicted yield at the VT-R1 stages (R2 = 0.78&ndash:0.83), Crop Circle and SPAD at VT (R2 = 0.57 and 0.49), and GreenSeeker at V10 (R2 = 0.52). When VIs were compared, SCCCI (R2 = 0.40&ndash:0.49) outperformed other VIs in terms of yield prediction. Overall, the best grain yield prediction was achieved using the MicaSense-derived SCCCI at the VT-R1 growth stages.
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