Measuring and preliminary modeling of drift interception by plant species

Currently, the concept of plant capture efficiency is not quantitatively considered in the evaluation of off-target drift for the purposes of pesticide risk assessment in the United States. For on-target pesticide applications, canopy capture efficiency is managed by optimizing formulations or tank-mixing with adjuvants to maximize retention of spray droplets. These efforts take into consideration the fact that plant species have diverse morphology and surface characteristics, and as such will retain varying levels of applied pesticides. This work aims to combine plant surface wettability potential, spray droplet characteristics, and plant morphology into describing the plant capture efficiency of drifted spray droplets. In this study, we used wind tunnel experiments and individual plants grown to 10–20 cm to show that at two downwind distances and with two distinct nozzles capture efficiency for sunflower (Helianthus annuus L.), lettuce (Lactuca sativa L.), and tomato (Solanum lycopersicum L.) is consistently higher than rice (Oryza sativa L.), peas (Pisum sativum L). and onions (Allium cepa L.), with carrots (Daucus carota L.) showing high variability and falling between the two groups. We also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants. The mean simulated drift capture efficiency rates were within the same order of magnitude of the mean observed rates of sunflower and lettuce, and differed by one to two orders for rice and onion. We identify simulating the effects of surface roughness on droplet behavior, and the effects of wind flow on plant movement as potential model improvements requiring further species-specific data collection

A Wind-Tunnel Assessment of Parameters That May Impact Spray Drift during UAV Pesticide Application

The objective of this study was to investigate the impact of varying wind speeds (1.5, 3.0, and 4.5 m/s), initial payload volumes (2 and 10 L), and nozzle droplet size characteristics (fine, medium, coarse) on drift during spray applications from an unmanned aerial vehicle (UAV) hovering freely in a wind tunnel. Along the length of the wind tunnel, glass slides were used to collect spray droplets at 14 points distributed in upwind, in-swath, and downwind distances. Analysis of the results showed that there are distinguishable shifts of up to 2 m in-swath as wind speed increases. Downwind of the UAV, a regression of the combined variables indicated that tunnel wind speed changed deposition the most overall, followed by nozzle/droplet size. Initial payload volume was less impactful. Overall, faster wind speeds, finer droplet sizes, and a heavier initial payload were associated with more drift on average. Wind directions and speeds were also measured on a finer scale of tunnel locations to record airflow pattern variability especially closer to the UAV. These findings may provide guidance to regulators and applicators to identify operating conditions for UAVs that limit off-target movement during applications