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.