Remote sensing is a key technology for precision agriculture to assess actual crop conditions; however, high-spatial-resolution imagery from aircraft and satellites are expensive. Unmanned Airborne Vehicles (UAV's) can be launched in narrow windows of good weather, fly large fields in preplanned patterns, and deliver the data rapidly to the user at lower cost. The Vector P from IntelliTech Microsystems, Inc. was fitted with two down-looking digital cameras, an up-looking quantum sensor, and computer controls based on GPS position. The internal filters of the cameras were removed and external narrow-band filters at 490 nm, 550 nm, 610 nm, 675 nm, and 800 nm were fitted onto the cameras. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for a given tarpaulin. When incident radiation was accounted for using the quantum sensor, the imagery matched the tarpaulin reflectances. For soybean, alfalfa and corn grown at the Beltsville Agricultural Research Center, dry biomass from zero to 120 g m-2 was linearly correlated to vegetation indices based on a visible band and the 800 nm band, but for biomass greater than 150 g m-2 in corn and soybean, these indices were saturated. Visual interpretation of the 800 nm imagery was useful in detecting weeds. There are many advantages of UAV's for precision agriculture, up to four cameras can be mounted in the Vector P for user-selectable narrow-band multispectral imagery.