Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 1, 2002
Publication Date: August 1, 2003
Repository URL: http://www.ars.usda.gov/sp2UserFiles/Place/36221500/cswq-0036-128359.pdf
Citation: CHUNG, S., SUDDUTH, K.A., DRUMMOND, S.T. DETERMINING YIELD MONITORING SYSTEM DELAY TIME WITH GEOSTATISTICAL AND DATA SEGMENTATION APPROACHES. TRANSACTIONS OF THE AMERICAN SOCIETY OF AGRICULTURAL ENGINEERS. 2003. V. 45(4). P. 915-926. Interpretive Summary: Grain yield mapping is a key process in precision farming, and many farmers are beginning to rely on yield maps to interpret field variability and make crop management decisions. During harvesting, after the grain is cut, it must travel through the combine mechanism for several seconds before it reaches the grain flow sensor. This delay time must be determined and accounted for to maintain the accuracy of the yield map making process. Current methods of estimating delay time require considerable effort, provide marginal results, or both. In this research, we applied two data analysis methods, geostatistics and data segmentation, to provide objective estimates of yield monitoring system delay time. Both methods performed well, generally estimating delay time to within 1 second of the more laborious current methods. Use of these methods could allow development of more accurate yield maps by providing improved delay time estimates, either ron a field-by-field basis, or for smaller within-field areas. This has th potential to benefit both agricultural advisors and the farmers who use yield maps in the decision-making process.
Technical Abstract: In combine harvesting, knowledge of the delay time from cutting the crop to grain flow sensing is required for accurate spatial location of grain yield data. Currently, delay time is usually either assumed or is determined by visual inspection of yield maps. Geostatistical and data segmentation methods were developed to estimate yield monitoring system delay time using gobjective criteria. The methods were validated with ideal data, and with mapped RTK-GPS elevation and soil EC data having known delay times. When applied to grain yield and moisture content measurements collected with a commercial yield monitoring system, the methods successfully estimated delay time for three test fields. In most cases, the results agreed, within +/- 1 s, with results achieved using a visual method. Grain yield and moisture content exhibited different delay times at different locations within the test fields. Thus, it may be appropriate to apply delay time corrections to homogeneous sub-field regions, instead of on a whole-field basis. Use of these new estimation methods could allow for more accurate and efficient processing of yield monitor data.