Measuring spray droplet size under unmanned aerial spraying system (UASS) downwash airflow using an image-processing-based liquid immersion method.

BACKGROUND: Accurate characterization of spray droplet size distribution is critical for optimizing pesticide application from unmanned aerial spraying systems (UASSs), as it governs deposition efficiency and off-target drift. However, conventional methods like water-sensitive paper (WSP) are prone to inaccuracies from droplet spread and coalescence, while in-flight laser diffraction is impractical for field-wide measurements under turbulent downwash conditions. This study developed and validated an image-processing-based liquid immersion method using silicone oil as the collection medium. A custom test apparatus was used to collect droplets from a UASS, and high-resolution images were analyzed to determine droplet size distributions. The method's performance was compared against WSP and photographic paper (PP) and validated against a laboratory-standard laser diffraction instrument.

RESULTS: Across five nominal droplet-size settings, SO-based Dv50 estimates showed a clear empirical association with laser diffraction and exhibited lower variability than WSP and PP. Droplet-size uniformity across the spray-width transect was higher for SO (CV 10-12%) than for WSP (16-22%) and PP (22-32%). In high-deposition regions where paper-based collectors tended to show inflated droplet sizes due to overlap/oversaturation, the SO method provided more consistent estimates.

CONCLUSION: The proposed SO liquid-immersion workflow offers a practical, cost-effective, and field-deployable option for characterizing UASS spray quality under realistic downwash airflow. This capability can support evidence-based optimization of nozzle settings and flight parameters to improve targeting performance while mitigating off-target drift. © 2026 Society of Chemical Industry.