Abstract
Airborne digital imagery is becoming an increasingly important data source for precision agriculture. In thisstudy, airborne digital imagery and yield monitor data were used to map plant growth and yield variability. Color-infrared(CIR) images were acquired from a grain sorghum field five times during the 1998 growing season, and yieldmonitor data were also collected from the field during harvest. The images were georeferenced and then classified intozones of homogeneous spectral response using unsupervised classification procedures. The images and unsupervisedclassification maps clearly revealed the consistency and change of plant growth patterns over the growing season.Correlation analyses showed grain yield was significantly related to the individual near-infrared (NIR), red, and greenbands of the CIR images and the normalized difference vegetation index (NDVI) for the five dates. Stepwise linearregression was also used to relate yield to the three bands for each of the five dates, and the three images obtained at andafter the peak growth produced higher R 2 -values (0.64, 0.66, and 0.61) than the other two early season images (0.39 and0.37). Yield maps generated from the three best images agreed well with a yield map from the yield monitor data. Theseresults demonstrated that airborne digital imagery can be a very useful data source for detecting plant growth and yieldvariability for precision agriculture.
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