Year One Impact-Ready Project Summaries
Diagnostic Technologies for On-Farm Decision-Making and Laboratory Surveillance in Dairy Systems
Principal Investigator: Matthias Wieland, Robert Hovey Udall Assistant Professor, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine
Summary: Aiming to help dairy farmers and veterinarians monitor livestock faster and more accurately, this research develops an artificial intelligence-enabled platform that combines on-farm testing, advanced laboratory diagnostics, and real-time data into a single, easy-to-use decision-support system. The platform allows for faster, more accurate information to support treatment decisions, improve disease monitoring, and identify emerging health threats across dairy farms. The project benefits U.S. farmers by lowering production costs through earlier disease detection, more efficient use of labor and antibiotics, and better-informed management decisions that improve herd health, productivity, and the long-term viability of dairy farming.
Working Robots on Farms: Immediate Impact Across NYS Diverse Cropping Systems
Principal Investigator: Christine Smart, the Goichman Family Director of Cornell AgriTech, Professor, School of Integrative Plant Science, College of Agriculture and Life Sciences
Summary: Demonstrating practical robotic, sensor-based, and AI-powered tools, this project helps fruit growers address labor shortages and rising production costs through automated mowing, precision spraying, pruning, irrigation, and crop monitoring. By reducing production costs while improving efficiency, the project will help U.S. fruit growers remain profitable, sustainable, and globally competitive. Cornell’s regional research and extension centers in New York state serve as hands-on technology hubs where growers see how these tools work together within real-farm conditions, determine which investments make sense for their farms, and provide feedback to help shape future technology development.
Cornell Center for Agricultural Intelligence and Robotics (CAIR)
Principal Investigator: Yu Jiang, Assistant Professor for Systems Engineering and Data Analytics for Specialty Crops, School of Integrative Plant Science, Cornell AgriTech, College of Agriculture and Life Sciences
Summary: Accelerating the transfer of new tools to farmers, the new Center for Agricultural Intelligence and Robotics (CAIR) at Cornell creates shared workshop and outdoor testing sites where researchers can quickly design, build, and assess robots and AI tools for growing fruits and vegetables. This, in turn, helps U.S. agricultural producers cut their input costs, ease labor shortages, and optimize their operations. The center also plans to engage in workforce development, training students to solve real-world problems by bringing robotic and AI technologies to working farms, where growers can provide feedback.
Empowering U.S. Poultry Farmers to Combat Highly Pathogenic Avian Influenza and Improve Profitability for Global Competitiveness
Principal Investigator: Diego Diel, Professor, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine; Xingen Lei, Professor, College of Agriculture and Life Sciences, Associate Dean of Research and Innovation
Summary: To protect poultry farms across the U.S. from highly pathogenic avian influenza (H5N1), veterinary, agriculture, and technology experts will test vaccination strategies, evaluate their costs and benefits, and develop better approaches to disease prevention beyond costly flock depopulation during outbreaks. The team also plans to use improved nutrition, feed supplements, and AI-enabled monitoring tools to strengthen bird health, detect disease earlier, and support more efficient production. Sharing results directly with poultry producers of all sizes – from backyard flocks to large commercial farms – the work supports healthier animals, more stable food supplies, and a stronger poultry industry.
AgriCore AI: Federated and Privacy-Preserving Agricultural AI Infrastructure
Principal Investigator: Hakim Weatherspoon, Professor, Cornell Anne S. Bowers College of Computing and Information Science
Summary: To strengthen the long-term productivity and competitiveness of American agriculture, AgriCore AI will help farmers securely pool the vast amount of data their farms generate every day, to provide insights, reduce costs, and improve efficiency. By using federated learning – a smart, privacy-first approach – this project lets farms tap into shared insights and better decision-making tools while keeping their data private.
Harnessing an AI-Driven Precision Technology Data Ecosystem to Increase Dairy Farm Profitability
Principal Investigator: Julio Giordano, Professor, College of Agriculture and Life Sciences
Summary: This project leverages advanced automated precision monitoring technologies and AI to help researchers and dairy farmers better understand how different novel management practices affect how dairy cows eat, produce milk, and stay healthy. Researchers combine fully automated real-time farm data with research on nutrition, health, and productivity to develop practical tools and strategies that help dairy farmers improve cow performance and economic efficiency. These research and infrastructure innovations will help U.S. dairy farmers make more informed decisions and increase profitability.
Ground Truthing Regenerative Agriculture: Leveraging Digital Technology to Protect Natural Resources, Build Resilience, and Enhance Economic Sustainability
Principal Investigator: Matthew Ryan, Professor, School of Integrative Plant Science, College of Agriculture and Life Sciences
Summary: By testing and improving regenerative farming practices, this research helps farmers use new digital tools on their own farms to develop strategies that build soil health, protect water quality, support biodiversity, and increase resilience to extreme weather. This allows U.S. farmers to identify practical ways to reduce production costs while increasing productivity and long-term sustainability. Combining on-farm research with yield monitoring, drones, satellite imagery, and precision agriculture technologies, the project helps farmers better understand what works, where it works, and how to make more informed management decisions.