Auburn University & USDA Launch Collaborative Drone and AI Research Initiative

Key Takeaways

• Auburn University's College of Agriculture is collaborating with USDA-ARS National Soil Dynamics Laboratory on drone and artificial intelligence research for agricultural applications.
• Steven Li, associate professor and Alabama Extension specialist, serves as principal investigator for the cooperative agreement research project involving multiple Auburn University departments.
• The research focuses on improving liquid and dry material delivery systems for drones used in Alabama's growing agricultural drone industry.
• Auburn University researchers are partnering with the University of Texas at Tyler's Department of Electrical and Computer Engineering to develop voice-controlled autonomous drone navigation and 3D cotton boll estimation technology.
• A drone cover crop seeding field day was conducted in December 2025 with Auburn University's Precision Ag Team, testing various application rates and operational parameters across three commercial drone platforms.

Auburn & USDA Research Collaboration Addresses Agricultural Technology Needs

The USDA-ARS National Soil Dynamics Laboratory at Auburn University has launched a comprehensive research initiative focused on advancing drone and artificial intelligence applications in agricultural systems. The collaborative effort involves Auburn University's College of Agriculture Crop, Soil and Environmental Sciences Department and Biosystems Engineering Department.

“As drone sensor technology, artificial intelligence capabilities, and payload capabilities evolve, the use of these technologies in agriculture will increase,” said Allen Torbert, research leader at the USDA-ARS National Soil Dynamics Laboratory on Auburn University's campus.

Auburn University Develops Advanced Drone Applications

The research encompasses multiple agricultural applications, including commodity crop seeding, pesticide applications, fertilizer delivery, and cover crop seedings. Recent field testing examined optimal operational parameters for drone-based cover crop seeding, with data analysis ongoing to provide recommendations for producers.