Planning: AI Ready; Artificial Intelligence for Agriculture (AI4Ag) Testbed

This project lays the foundation for AI4Ag, an AI-ready living lab for agriculture, where scientists, students, and technology developers will collaborate to create artificial intelligence (AI) systems that support food production and improve farming efficiency. As the global agri-food system increasingly relies on data-driven technologies, stakeholders, from farmers and industry to consumers (and even crops and livestock) stand to benefit. AI driven innovations have the potential to address major challenges including labor shortages, food safety, disease and pest management, and weather variability. However, many farms struggle to adopt advanced technologies due to high costs, limited infrastructure, and the complexity of agricultural systems. Hosted at Cornell University, AI4Ag aims to overcome these barriers by creating a shared, accessible space equipped with tools, data, and expertise to support AI development and testing in real-world settings. The project will also foster a community of researchers and students, helping to train the next generation of innovators in agriculture. By making it easier to test, refine, and share new ideas, AI4Ag will unlock the potential of AI to transform agriculture and contribute to a more sustainable and resilient US food system. This project establishes the foundation for AI4Ag, an AI-ready living lab embedded within Cornell Agricultural Systems Testbed (CAST), designed to accelerate the development and deployment of AI technologies in agriculture. CAST, operated under the Cornell Institute for Digital Agriculture (CIDA), provides a robust platform with sensing infrastructure, live and archived data streams, and interdisciplinary expertise across animal science, crop science, engineering, and computing. AI4Ag will lower barriers to entry for researchers, industry, and practitioners by enabling real-world testing of AI solutions in complex agricultural environments. The project will develop a strategic plan and governance structure, including an executive management team, testbed management team, research steering committee, and advisory board, to guide AI innovation. To drive this effort, the first objective is to build a multidisciplinary user community. Second, is to assess and enhance the CAST infrastructure. Third, is to demonstrate AI-readiness through data integration. Fourth, is to establish operational frameworks for external access. Expected AI innovations include a suite of state-of-the-art AI tools and methodologies, including large language models and agentic AI frameworks addressing agriculture-specific tasks. AI4Ag will serve as a replicable model for AI-enabled living labs, fostering participation and training the next generation of AI and agri-food researchers. By aligning infrastructure with user needs and demonstrating feasibility, this project will catalyze sustainable, scalable AI innovation across the agri-food system. 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.