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Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASAE.  VOL. 43(6): 1927-1938 . (doi: 10.13031/2013.3098) @2000
Authors:   C. Yang, J. H. Everitt, J. M. Bradford, D. E. Escobar
Keywords:   Image classification, Precision agriculture, Remote sensing, Spatial variability, Yield monitor

Airborne digital imagery is becoming an increasingly important data source for precision agriculture. In this study, 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 yield monitor data were also collected from the field during harvest. The images were georeferenced and then classified into zones of homogeneous spectral response using unsupervised classification procedures. The images and unsupervised classification 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 green bands of the CIR images and the normalized difference vegetation index (NDVI) for the five dates. Stepwise linear regression was also used to relate yield to the three bands for each of the five dates, and the three images obtained at and after the peak growth produced higher R 2 -values (0.64, 0.66, and 0.61) than the other two early season images (0.39 and 0.37). Yield maps generated from the three best images agreed well with a yield map from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful data source for detecting plant growth and yield variability for precision agriculture.

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