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High-resolution color infrared (CIR) images acquired with an airborne digital camera were used to detect infield spatial variability in soil type, crop nutrient stress, and to analyze spatial variability in yield. Images were processed using an unsupervised learning (clustering) method. The clustered images were geo-referenced, and spatially analyzed using a GIS package. The image patterns in a processed image of bare soil matched well with soil type map with 76% accuracy. The CIR images of a cornfield indicated nitrogen stress areas from 75 days after planting (DAP). The CIR reflectance was better correlated to the yield after pollination of corn compared to the early images. The spatial variation in yield was linearly correlated to the spatial variation of individual reflectance bands (NIR, R, and G) as well as normalized intensity (NI) of CIR image. Spatial yield models on uncalibrated reflectance bands of image could predict 76 to 98% of yield variation in each field. A linear regression model on NI developed from one field image predicted yield with an accuracy of 55 to 91% in different fields and seasons. Digital aerial imaging proves to be a promising tool for obtaining spatial in-field variability in the crop field for site-specific management and yield prediction.