Year One Faculty Research Project Summaries
From Orchard Block to Packing Line: Developing CANOPY, CORE, and HARVEST as AI Tools for Precision Apple Management
Principal Investigator: Jason Londo, Associate Professor, School of Integrative Plant Science, Cornell AgriTech, College of Agriculture and Life Sciences
Summary: Moving a small percentage of apple growers’ harvest into higher size and color grades can earn them hundreds to thousands of dollars more per acre. This project will link season-wide drone maps of tree health and crop load in Gala and Honeycrisp orchards to the size, color, and fruit-quality data that packing lines collect every year on millions of apples. The AI tools built by merging these data will show growers where they can optimize fertilizer use to lower costs, prevent waste, and raise fruit quality.
Scaling Agronomic Data Collection for Agricultural AI
Principal Investigator: Wendy Ju, Professor, Cornell Tech
Summary: To help farmers reduce time spent on compliance paperwork and to optimize their operations, this project aims to help farmers gain intelligence about their own farm operations by using a voice assistant to verbally collect agronomic data in tractor cabs. The researchers are developing this system on small and mid-sized farms and will study how it changes record-keeping and decision-making.
From Soil to Infant Diet: Mechanisms Linking Agricultural Systems, Crop Traits, and Nutritional Safety
Principal Investigator: Olena Vatamaniuk, Professor, School of Integrative Plant Science, College of Agriculture and Life Sciences
Summary: To help U.S. farmers produce safer, higher-value crops, this project will identify how soil conditions, farming practices, and genetics influence the accumulation of beneficial nutrients and potentially harmful metals in sweet potato tubers and food products. Working with industry and extension partners, the researchers will develop a framework that links agricultural management decisions to nutrient density, toxic metal content, and food safety, while uncovering the biological mechanisms that cause sweet potatoes to absorb and store metals.
Innovative Fresh Cow Feeding Strategies to Enhance Milk Production Efficiency in American Dairy Farming
Principal Investigator: Sabine Mann, Associate Professor, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine
Summary: Researchers have previously shown that utilizing a novel feeding strategy focused on protein supply to cattle during early lactation significantly improves milk production efficiency. This project aims to determine how long cows need to stay on this diet for it to be effective, and whether the strategy differs for cows lactating for the first time. This readily adoptable dietary strategy requires no additional technology, meets cows' dietary needs to maintain health and productivity, and sustainably improves efficiency for U.S. farmers facing rising costs and tight margins.
SMART-Drain: A Testbed for Automated Agricultural Drainage Management to Improve Water Use Efficiency and Quality
Principal Investigator: Matthew Reid, Associate Professor, School of Civil and Environmental Engineering, Cornell David A. Duffield College of Engineering
Summary: Advancing water-management strategies to help farmers manage drought, provide nutrients to crops, and minimize agricultural water pollution, this project establishes SMART-Drain, a field-scale research facility at Cornell AgriTech that uses environmental sensing and real-time control of automated drainage gates to optimize water retention and nutrient availability. The project will also investigate how automating water flow and oxygen levels in woodchip bioreactors can improve treatment of nutrients and organic contaminants in agricultural drainage.
HARVEST: Harnessing Agricultural Residuals for Value-added Energy Using Sustainable Burner Technologies
Principal Investigator: Sadaf Sobhani, Assistant Professor, Sibley School of Mechanical and Aerospace Engineering, Cornell David A. Duffield College of Engineering
Summary: With a focus on dairy farms, this project will develop burners that convert gas generated from agricultural waste into useful heat rather than wasting it through conventional flaring. This technology could help U.S. farmers lower energy costs for heating water and drying bedding, while improving farm efficiency and reducing emissions. Using specially designed ceramic structures, researchers aim to make the burners more reliable and efficient, even when the gas supply fluctuates.
Corn Silage Hybrids and Fungicide Programs: Managing Disease, Mycotoxins, Yeast, and Forage Quality
Principal Investigator: Lindsey Becker, Assistant Professor, School of Integrative Plant Science, College of Agriculture and Life Sciences
Summary: To help lower production costs, this project will study how corn varieties, growing conditions, and disease-management decisions influence mycotoxin contamination, spoilage risk, and the nutritional quality of corn silage – a fermented feed for dairy cattle. Researchers will compare more than 40 commercial corn varieties, examine yeasts and molds from harvest through fermentation, and test whether organic and conventional disease-control products applied by drone improve feed quality.
The Nature Dividend: Translating Water Quality Improvements from Regenerative Agricultural Practices into Profitable and Sustainable Farming
Principal Investigator: Catherine Kling, Tisch University Professor, Charles H. Dyson School of Applied Economics and Management, Cornell SC Johnson College of Business, College of Agriculture and Life Sciences
Summary: Fertilizer and manure runoff from farm fields cause over $50 billion in water pollution damage each year, but regenerative practices are proven to help keep those nutrients on the land. This project will survey farmers and map pollution damage to find where these practices deliver the most benefit at the lowest cost. This will help U.S. farmers cut input costs, farm more efficiently, and earn more from conservation programs.
Advancing U.S. Calf Rearing Through Digital Driven Management
Principal Investigator: Francisco Leal-Yepes, Assistant Professor, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine
Summary: This project uses emerging technologies to help dairy and beef farmers improve calf health, survival, and welfare while working more efficiently. Innovative tools, including robotic systems that automatically monitor calf behavior, can help farmers and workers find early signs of disease, reduce monitoring time and costs, and provide calves with timely care.
Biomarker Discovery for Salmonella Dublin Diagnosis and Treatment
Principal Investigator: Laura Goodman, Associate Professor, Department of Public & Ecosystem Health, College of Veterinary Medicine
Summary: This project will focus on Dublin, a hard-to-prevent and almost fully antibiotic-resistant Salmonella affecting dairy farms across the U.S. and causing severe bloodstream disease in calves and humans. Researchers will partner with the New York State Cattle Health Assurance Program and Cornell diagnostic and biotechnology centers to implement a precision testing strategy for producers while studying the genetic fingerprints that explain why this germ causes such severe disease. The goal is to provide a test that can aid veterinarians and farmers in responding to outbreaks and implementing control programs, while building the foundation to eventually find a cure.
Novel Genomics Technologies to Enable Plant Breeding Programs at Scale
Principal Investigator: Jen Grenier, Director, Biotechnology Resource Center Genomics Facility
Summary: To help guide breeding decisions that improve farming outcomes, this project will develop novel technologies to produce comprehensive genetic datasets, scalable to thousands of samples across a wide variety of crop species, to be launched as services by Cornell’s Genomics Facility at the Biotechnology Resource Center. In partnership with collaborators at Cornell and USDA Agricultural Research Service, researchers will use these technologies to characterize genetic variation within agricultural repositories to help improve characteristics such as yield, appearance, robustness, disease resistance, and environmental resilience.
A Unified Network Infrastructure to Accelerate Digital Agriculture Technology Adoption for American Farmers
Principal Investigator: Max Zhang, Irving Porter Church Professor of Engineering, Cornell David A. Duffield College of Engineering
Summary: To help farmers lower production costs by reducing duplicative efforts and improving data, this project develops an affordable and shared farm network that integrates low-power wireless communication, positioning services with the accuracy each job needs, and tools to plan coverage, adjust network operation, and diagnose problems. Researchers will conduct tests in dairy operations, apple orchards, and vineyards to evaluate how well one network can support both routine monitoring and precision operations, such as equipment guidance.