Corn and soybean yield indicators using remotely sensed vegetation index

Precision farming involves crop management in parcels smaller than field size. Yield prediction models based on early growth stage parameters are one desired goal to enable precision farming approaches to improve production. To accomplish this goal, spatial data at a suitable scale describing the variability of yield, crop condition at certain growth stages, soil nutrient status, agronomic factors, moisture status, and weed-pest pressures are required. This paper discusses the potential application of aerial imaging to monitor and predict the potential yield for corn and soybean at various growth stages in the season. Included in the analyses were aerial images, yield monitor data and soil grid sampling. The relationship between remotely sensed Normalized Difference Vegetation Index (NDVI) and yield was best at 9 m spatial resolution. Preliminary results indicate that it is possible to use NDVI to estimate the potential yield for soybean and corn when canopy reaches full cover.