Uas-based aerial herbicide spray technology for row-crop weed control
Unmanned Aerial System (UAS) spray technology for precise herbicide application in row crops, offering 95% late-stage weed control efficacy versus 70% with backpack sprayers. Enables $10-25 per acre savings, 80-90% herbicide reduction through spot spraying, and includes a 100K-image weed recognition dataset for machine learning-driven precision agriculture.
Overview
This solution leverages Unmanned Aerial System (UAS) technologies to apply contact herbicides in row crops such as corn, cotton, and soybean. Field validation across multiple growing seasons has demonstrated that UAS-based spray applications achieve significantly greater weed control efficacy than conventional backpack and tractor sprayers, particularly at late postemergence stages. The technology offers potential per-acre cost savings of $10-25, plus 80-90% herbicide reduction when combined with spot-spray applications. The research team is also developing machine learning-based weed recognition capabilities to enable precision spot spraying based on geo-coordinates and automated weed identification.
Technical specifications
Validated performance:
- 90-95% weed control at early postemergence (POST), comparable to backpack sprayers
- 95% weed control at late-POST, significantly outperforming backpack sprayers at 70%
- Tested with glufosinate at 1X rate using low carrier volumes of 2 and 4 gallons per acre (GPA), compared to 15 GPA with conventional sprayers
- 30% greater efficacy of contact herbicides versus backpack spray applications due to improved spray droplet distribution on weed leaves, including lower leaf surfaces
Key research and optimization areas:
- Standardization of operational parameters including flight speed, spray pressure, altitude, and nozzle selection
- UAS-based spot-spray applications using weed geo-coordinates
- Machine learning-based weed recognition using a dataset of 100K images covering Palmer amaranth and common ragweed
- Evaluation of droplet spectra, crop injury, weed control efficacy, and operational economics
Technology readiness level
The technology has progressed through multi-year field validation (2020-2022) across corn, cotton, and soybean cropping systems. Current efforts focus on refining nozzle configurations and operational parameters, then integrating machine learning-driven weed identification with automated spot-spray capabilities. Future validation will assess the efficiency and precision of auto-operated UAS spot-spray systems combined with blanket spray best practices for full-spectrum deployment in commercial row-crop production.
About Virginia Tech
Virginia Polytechnic Institute and State University (Virginia Tech) is a comprehensive public land‑grant research university with broad graduate and professional programs and a strong industry orientation. An adjacent research and technology park links companies with faculty, shared labs, and prototyping resources, while facilities in the National Capital Region create a direct connection to federal partners and supply‑chain collaborators. Integration with a regional health system supports clinical research and translational pathways, and the statewide extension network enables field deployment and validation with industry and communities. Research is supported by competitive federal funding from agencies such as NSF, NIH, DOE, USDA, and the Department of Defense. A dedicated technology transfer office provides IP management, licensing, startup formation, and industry contracting support.
delivered to your inbox.