Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2012
Publication Date: 6/18/2012
Citation: Lee, D., Sudduth, K.A., Drummond, S.T., Chung, S., Myers, D.B. 2012. Automated yield map delay identification using phase correlation methodology. Transactions of the ASABE. 55(3):743-752. Interpretive Summary: Yield maps are a key component of precision agriculture, used both in developing and evaluating precision management strategies. Ideally, software that generates grain yield maps from the raw data provided by combine yield monitors should automatically correct common errors associated with machine and operating characteristics. Perhaps the most basic correction required is to properly compensate for the time lag between the cutting of the crop from the field and the measurement of the grain flow by the sensor in the combine. This time lag varies between machines and also varies with changes in operating conditions. With software we previously developed, users can iteratively examine different time lag values and select the best one. However, good results require an experienced user and thus an automated method of determining time lag would be preferable. In this paper we discuss one such automated method, based on techniques from the field of image processing. The new method worked well, providing accurate results in the majority of test cases. The new method runs quickly on a desktop computer and has potential for use in an integrated software package. This research is a step toward an integrated procedure to clean yield data, a result that would improve data quality for both researchers and producers involved in precision agriculture. Improved yield map data will allow more accurate development and assessment of precision management strategies for improved farm profitability and environmental protection.
Technical Abstract: Crop yield data is a key component of precision agriculture, critical for both development and evaluation of precision management strategies. Ideally, software that generates grain yield maps from raw yield monitor data should automatically correct errors associated with machine and operating characteristics. Perhaps the most basic correction required is to properly compensate from the time lag (or position lag) between the cutting of the crop from the field and the grain flow measurement by the flow sensor in the combine. Past research has suggested several approaches to automatically determine delay time but for various reasons these have not been implemented in mapping software. In this paper we present a new, computationally efficient method that can accurately determine delay time for individual fields using the image processing method of phase correlation. The phase correlation delay identification (PCDI) method was evaluated using a number of yield maps with varying degrees of harvest complexity and results were compared to a geostatistical method. The PCDI method produced accurate estimates of delay time in approximately 90 percent of test datasets, and provided a way to evaluate the reliability of the estimate. Additionally, the PCDI method was more computationally efficient than previous methods. Results of this study will increase the feasibility of including automatic delay time compensation in yield mapping software.