Yield Data Filtering Techniques for Improved Map Accuracy

Yield mapping has become an important function in sitespecific management systems. Decisions of economicsignificance 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 yieldestimate at any given point is affected by a number of factors, including the number of combines used in a field, the shapeof the field, yield and moisture calibration, harvest pattern, and operator practices. By relying on limits and patterns in yielddata, 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 filteringtechnique for exported yield files was developed and tested on 10 fields of corn, sorghum, and rice. The inaccurate yield pointswere identified with filter functions based on yield limits, moisture limits, travel distance, yield surges, and less than fullheader width. This filtering algorithm resulted in a higher field average and lower standard deviation than either theunfiltered data or data filtered with maximum and minimum thresholds alone. The yield data filter removed up to 11% of thedata points, with yield distributions being primarily affected at the upper and lower extremes. The filter was judged successfulin improving yield map accuracy.