The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to quantitatively assess farmland status and resilience. This project studies varying agricultural productivity subject to weather and static soil and landscape properties. This study will develop models using artificial intelligence to make predictions. It will be based on data from the U.S. and other agriculturally-intense regions of the world, allowing the generalization of the framework into new geographical domains. The models will also be tested on a variety of staple crops, such as corn, soybean, wheat, and rice.This system will include an interface for untrained users, as well as important data for quantitative financial analysis. This SBIR Phase I project aims to develop an integrative machine-learning framework consisting of state-of-the-art generative weather models, feature translation models, and crop yield models to obtain a rich ensemble of simulated growing season environmental conditions, and associated yield estimates. Supported by high-resolution satellite imagery in the past 20 years, the model is not only capable of capturing granular yield variability at the field scale, but also the heterogeneity of productivity within a crop field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
