Planning: AI-Ready: Cyber-Agricultural Systems Testbed (AI-AgARENA)

About This Grant

The AI-AgARENA project will develop a national-scale planning roadmap for an artificial intelligence (AI)-ready agricultural research testbed. The proposed infrastructure is designed to support the development and testing of advanced AI technologies in real-world farming environments, enabling breakthroughs in resilient agriculture. The project will unify geographically distributed testbeds across Iowa, Arizona, and Ohio, allowing researchers to evaluate AI methods across a range of crops, soil types, and climatic conditions. By integrating autonomous robotics, wireless networking, and multimodal sensing with cyberinfrastructure, AI-AgARENA aims to democratize access to agricultural data and test environments for a broad research community. The project addresses pressing challenges in food security and agricultural resilience while supporting U.S. leadership in emerging AI and wireless technologies. The planning effort will engage scientists, educators, industry stakeholders, and government agencies to ensure the testbed is extensible, and responsive to national priorities. Education, outreach, and workforce development are integral to this effort, with special attention to training future AI-agriculture innovators. This work supports NSF’s mission by advancing science and technology, strengthening U.S. economic competitiveness, and promoting societal well-being through sustainable food systems. The project will plan and design AI-AgARENA, an AI-ready cyber-physical testbed that enables deployment, validation, and benchmarking of AI tools in real agricultural settings. The effort will integrate existing agricultural infrastructure (such as sensing platforms, drones, robots, and communication networks) across three institutions, coordinated via shared cyberinfrastructure built on the NSF-funded CyVerse platform. The planning activities include team formation and governance design; prototyping cyberinfrastructure for real-time data access and model deployment; defining financial sustainability models for long-term operation; developing a community engagement strategy through workshops, surveys, and early demonstrations; and identifying use cases for AI research in plant phenotyping, autonomous field operations, multi-agent coordination, and digital twins for crop management. The testbed will be designed to support high-throughput multimodal data collection, AI model training and inference, secure and reproducible experimentation, and shared access for academic, industry, and agency partners. Outcomes will include a well-scoped implementation plan, beta-tested prototypes, and a set of priorities aligned with the needs of the AI, agriculture, and wireless research communities. 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.