Yield data filtering techniques for improved map accuracy
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Yield mapping has become an important function in site-specific management systems. Decisions of economic significance are made based upon the patterns and summary statistics of the yield data recorded during harvest. Unfortunately, yield maps frequently contain data points that are not accurate estimates of the yield at that point. The yield estimate at any given point is affected by a number of factors, including the number of combines used in a field, the shape of the field, yield and moisture calibration, harvest pattern, and operator practices. By relying on limits and patterns in yield data, filtering can be done prior to mapping to remove some of the problems. Yield data generated by producer cooperators was obtained for use in several research projects. An unsupervised filtering technique for exported yield files was developed and tested on 10 fields of corn, sorghum, and rice. The inaccurate yield points were identified with filter functions based on yield limits, moisture limits, travel distance, yield surges, and less than full header width. This filtering algorithm resulted in a higher field average and lower standard deviation than either the unfiltered data or data filtered with maximum and minimum thresholds alone. The yield data filter removed up to 11% of the data points, with yield distributions being primarily affected at the upper and lower extremes. The filter was judged successful in improving yield map accuracy.
author list (cited authors)
Beck, A. D., Searcy, S. W., & Roades, J. P.