Jordan Bell (ST11) was notified last week that his FY22 Center Innovation Fund proposal was selected. The proposal titled, “Machine Learning for Agriculture Crop Damage Identification and Assessment from Intense Thunderstorms”, will develop machine learning (ML) models for the automated identification of severe weather damage to agricultural crops through the use of daily True Color from NASA satellites. Deliverables of this proposal include: 1) detailed training data sets of known hail damage scars comprising a record of 20 years of NASA imagery, 2) machine learning-based models and related code, and 3) methods for automatically identifying damaged areas. Mapping of sudden, land surface change detection benefits Earth Science remote sensing and applications while overall, new techniques in ML approaches can be extended further to planetary topics. Through the implementation of ML, the goal is to increase the efficiency and accuracy of detection of these damaged areas for additional analysis. This project will utilize the MSFC Hail Damage Swath Events Database for training and validation. Andrew Molthan and Brian Freitag (ST11) will serve as Co-Is on this this CIF.