Evaluation of Spray Drift of Plant Protection Drone Nozzles Based on Wind Tunnel Test

Abstract

The use of drones in agriculture is expanding at a brisk pace in crop production due to the superiority in precision, efficiency, and safety of their applicators. However, their potential drift risk also raises concern for users and regulatory authorities. The method of wind tunnel research can effectively evaluate the weighted influence of each drift factor, especially the drift characteristics of the nozzle and spray solution. Based on the wind tunnel test results, centrifugal nozzles have a higher drift risk than hydraulic nozzles, even with a similar DV50. The cumulative drift rate of the centrifugal nozzle at 2 m downwind was 90.1% compared to the LU12001 nozzle’s 40.6% under the wind speed of 3.5 m/s. Compared with the same coding as the flat fan hydraulic nozzle, the IDK nozzle can effectively reduce the drift rate. For the tested nozzles, DV50 and wind speed had a linear relationship with drift rate, and the sampling location had an exponential or logarithmic relationship with drift rate. Spray adjuvants, especially modified vegetable oils, had a significant effect on reducing the amount of drift. The results of this experiment provide a reference for the selection of nozzles and the addition of spray adjuvants. Further clarifying the spray drift characteristics of drones until a drift prediction model is available is still the focus of research.

The use of unmanned aerial vehicle for controlling Oryctes rhinoceros beetle on Immature Oil Palm

“…(2023), spray drift is significantly affected by wind speed, nozzle type and nozzle coding [10]. Drone spraying was affected by the wind when the velocity reached 10 km/h.…”

“…Some of the recorded velocities were 9.35 km/h, 11.15 km/h, and 11.51 km/h, therefore spraying was postponed for a few minutes. Nevertheless, the use of drone sprayers has the potential to be implemented in hilly areas, reduce chemical exposure to the operators, and allow precise spot spraying [10].…”

Spray droplet field reconstruction based on synthetic aperture imaging

“…In plant protection drone spray fields, liquid droplets are propelled from nozzles by high-pressure airflow and subsequently undergo drifting. This drifting phenomenon is influenced by the ambient airflow, with longer suspension times within the airflow enhancing the likelihood of droplet displacement from the intended target due to aerodynamic forces [4] . The dynamics of droplet drift reveal the inherent three-dimensional nature of the spray field.…”

“…The inputs of the neural network model include height, flow rate, pressure and wind speed. Existing experimental methodologies involve deploying hygroscopic paper [6] or samplers of the monofilament line [4] near the target to register droplet impacts. These technologies cannot provide real-time reflection of the overall structure of the spray field.…”

Toward Virtual Testing of Unmanned Aerial Spraying Systems Operating in Vineyards

“…The interaction between spray and rotors has been experimentally tested both in the open field [12] and in wind tunnel facilities [13][14][15], where the authors investigate drift with varying wind speed, spray volume, and nozzle characteristics. Drift is also addressed in [16], where the authors survey solutions to reduce the inevitable drift by optimizing the rotor, nozzle setup, spray system, and flight parameters.…”