Analysis of the spatial and temporal distribution of a spray cloud using commercial LiDAR

Pesticide spray drift is an important cause of environmental damage. It can also cause phytotoxicity in non-target areas, and reduce efficacy. Suitable methods for evaluating the spray drift are necessary to reduce these hazards and improve the utilisation of pesticides. However, most conventional spray drift evaluation methods based on sampling patterns that are labour-intensive, expensive, and time-consuming. Moreover, the temporal dis-tribution information of drifting droplets cannot be easily obtained. In this study, a spray cloud evaluation method is investigated based on a common commercially available light detection and ranging (LiDAR) system. Its feasibility was verified in terms of evaluating a spray cloud, and the effect of the wind speed on the relationship between the number of cloud points and deposit volume was experimentally studied. The optimal scanning area of the LiDAR was explored and a simple statistical model developed based on the experi-mental data developed. The model was validated and had a high coefficient of determi-nation (R2 1/4 0.71). The deposit volume predicted by the model also exhibited a high correlation coefficient (r 1/4 0.89) and coefficient of determination (R2 1/4 0.79) with respect to the measured deposit volume on the collector, and it satisfies the significance test at the 0.01 level. The LiDAR point cloud facilitated an intuitive analysis of the spatiotemporal distribution of drifting droplets in the air, which is difficult to achieve using passive sampling.(c) 2022 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

Pesticide spray drift is a significant cause of environmental damage and requires suitable evaluation methods to improve pesticide utilization. This study investigates a spray cloud evaluation method using a LiDAR system, validates its feasibility, and develops a statistical model for predicting deposit volume.