High-Resolution Methods of Yield Prediction

Abstract

As agricultural machinery continues to grow in size to help feed a growing population, producers must rely on technology to provide strategic insights into crop performance and health both during the growing season and at harvest. Specifically, geo-spatial data helps overlay crop performance in comparison to factors such as soil metrics or other operation specific equipment metrics. This research specifically focused on understanding a potential valuable input variable that would provide greater insight into a more accurate yield calculation across larger row crop harvesting platforms. Due to the intellectual property generated by this research, the results are unable to be released publicly. The full research, including technical details and results, has been evaluated by the major professor.