The success of precision agriculture requires data, analytics, and automation.  Rapid growth in all three areas has been rapid over the last few years, and this is particularly true in the realm of data, with many new sensors and sensor platforms now available to provide “big data.”  Fixed-wing UAVs have been viewed as a new platform for data collection that can provide flexible, inexpensive, high-resolution image data over large fields in a reasonable amount of time.  For high-resolution remote-sensing images from fixed-wing UAVs to provide actionable information for precision agriculture, the data derived from them must be readily accessible and reliable.  Commercial systems and most research programs do not currently provide means for (1) fully automated mosaicking and calculation of spectral indices and plant height, or (2) calibrating these data.  However, recent research at Texas A&M University has been conducted to develop a system of combined ground-control points (GCPs) with reflectance targets and height-calibration targets that enable rapid, automated, radiometric calibration for calculation of accurate spectral indices and height calibration for calculation of accurate plant-height data.  Each GCP provides an RTK-GPS position reference, multiple reflectance targets spanning the expected dynamic range in the multispectral image data acquired, and multiple platforms at known heights above ground that span most of the expected dynamic range in the plant-height data.  Software has been developed to radiometrically calibrate an image mosaic automatically as follows: (a) ingest an image mosaic created for a given field, (b) automatically find the GCPs in the image data, (c) extract digital numbers (DNs) from the reflectance targets, (d) create the DN-to-reflectance calibration function, and (e) calibrate the image mosaic for reflectance.  The software can also calibrate plant-height data as follows: (f) ingest a surface model based on structure from motion across the mosaic, (g) extract vertical positions of the known-height platforms on the previously located GCPs, (h) create the estimated-to-actual height calibration function, and (i) calibrate the surface model across the mosaic for height.  Calibrated reflectance data have been shown to be much more accurate than uncalibrated data, having an average error less than 3%.  The calibration of plant-height data has been shown to reduce the error in surface models by 20%.  This combined system of physical GCPs with reflectance and height targets, along with software for automated processing, has the potential to provide accurate reflectance-based spectral indices and plant-height data across large fields rapidly after image acquisition.