This report examines the current and emerging applications of AI in agriculture, including crop and livestock monitoring, precision management, yield prediction, disease and pest detection, automation, and supply chain optimization. It explores the data sources, algorithms, and digital infrastructure that underpin AI-driven systems, as well as the role of AI in enhancing sustainability, productivity, and resilience under climate and resource constraints.

The next generation of agricultural problem-solving requires large-scale data sharing across datasets and disciplines. Limited data accessibility is currently a major barrier to better decision-making. This paper identifies four imperatives for open access to public agricultural research: best practices for data workflows in federally funded projects; mechanisms for sharing grey and dark data; coordination among existing data networks; and sustainable infrastructure to curate and preserve data. The authors propose that funding agencies pay for data stewardship in proportion to grant volume, and outline strategies for bridging expertise gaps, reorienting institutions, and leveraging legacy assets.