Location: Aerial Application Technology Research
Project Number: 3091-30500-001-005-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 1, 2026
End Date: Jun 30, 2028
Objective:
The objective of this agreement is to establish standardized, science-based field
protocols and an integrated relational data framework to improve evaluation, modeling, and validation of spray drift from unmanned aerial vehicle (UAV) pesticide applications. The rapid expansion of UAV spray systems has created an urgent need for harmonized methodologies that ensure data comparability, reproducibility, and regulatory defensibility across studies conducted in different crops, environments, and geographic regions. Specific objectives are to: (1) develop standardized UAV drift field protocols defining minimum meteorological, operational, and spray-system parameters necessary for regulatory-grade assessment; (2) design and implement a structured relational database architecture capable of integrating heterogeneous multi-state field datasets, including droplet spectra, deposition measurements, meteorological observations, and flight metadata; (3) conduct spray transport model simulations using AgDISPro and related tools to evaluate predictive performance across UAV platforms and environmental conditions; (4) perform supplemental droplet size characterization testing as needed to support accurate model parameterization; and (5) generate shared research resources, documentation, and peer-reviewed dissemination products that strengthen scientific collaboration and model improvement efforts.
Approach:
The project will proceed through coordinated phases of protocol development, database construction, modeling analysis, and dissemination. A review will be done of existing UAV drift literature, international standards, and recent domestic field trial experience to draft a harmonized protocol specifying required calibration procedures, flow-rate verification, droplet size documentation, flight-path recording, meteorological monitoring, sampling materials, replication thresholds, and metadata structure. Emphasis will be placed on traceability between field measurements and model inputs to ensure suitability for research and regulatory use. An Excel-based data-entry framework will standardize capture of UAV operational parameters, spray mixture characteristics, droplet size distributions, environmental conditions, and downwind deposition data. Following structured quality assurance procedures, field datasets will be imported into a Python-based relational database linking study-level metadata, exposure measurements, derived variables, and modeling outputs. Automated scripts will generate model-ready input files for AgDISPro and related drift models, reducing manual processing errors and improving reproducibility. Model simulations will be conducted to replicate field conditions across multiple UAV platforms and crop canopies. Predicted deposition patterns will be compared to measured tracer-based deposition using statistical and graphical methods to quantify agreement and identify systematic deviations. Sensitivity analyses will evaluate the influence of flight height, rotor configuration, droplet size, and environmental parameters on model performance. Where droplet spectra are incomplete or uncertain, low-speed wind tunnel testing will be conducted to provide calibrated inputs for modeling. Protocols, database tools, model workflows, and summary analyses will be made available to collaborators through shared repositories, and findings will be synthesized into one or more peer-reviewed publications.