1. A Machine Learning Framework for Automated Anomaly Detection in Precision Agriculture Geospatial Data

Modern precision agriculture relies on the analysis of geospatial data generated by a wide range of equipment and sensors. While these datasets are foundational to data-driven management practices, they are often affected by inaccuracies arising from various sources. Existing filter systems, such as Yield Editor and Map Filter, that implement operational (e.g., abrupt changes in speed, speed limits, and removal of maneuvers), global statistical (e.g., observations that are inconsistent with the... Z.C. Komarnisky, F. Hoffmann Silva Karp