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

Abstract

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 distribution 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 experimental data developed. The model was validated and had a high coefficient of determination (R 2 = 0.71). The deposit volume predicted by the model also exhibited a high correlation coefficient (r = 0.89) and coefficient of determination (R 2 = 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. • Commercial LiDAR system used to evaluate pesticide spray cloud. • Number of LiDAR cloud points correlated with volume measured on polyethylene lines. • Spatiotemporal distribution of drifting spray can aid suitable buffer zone setting. • Laboratory experiments explore optimal scanning area for LiDAR. • Results validate the feasibility of model to predict spray volume airborne.