Spray drift evaluation with point clouds data of 3D LiDAR as a potential alternative to the sampling method

Longlong Li; Ruirui Zhang; Liping Chen; Boqin Liu; Linhuan Zhang; Qing Tang; Chenchen Ding; Zhen Zhang; Andrew J Hewitt

doi:10.3389/fpls.2022.939733

Spray drift evaluation with point clouds data of 3D LiDAR as a potential alternative to the sampling method

Front Plant Sci. 2022 Jul 18:13:939733. doi: 10.3389/fpls.2022.939733. eCollection 2022.

Authors

Longlong Li^{1

2

3}, Ruirui Zhang^{1

2

3}, Liping Chen^{1

2

3}, Boqin Liu⁴, Linhuan Zhang^{1

2

3}, Qing Tang^{1

2

3}, Chenchen Ding^{1

2

3}, Zhen Zhang⁵, Andrew J Hewitt⁶

Affiliations

¹ Research Center of Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China.
² National Research Center of Intelligent Equipment for Agriculture, Beijing, China.
³ National Center for International Research on Agricultural Aerial Application Technology, Beijing, China.
⁴ College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling, China.
⁵ College of Information Science and Engineering, Shandong Agricultural University, Taian, China.
⁶ Centre for Pesticide Application and Safety, The University of Queensland, Brisbane, QLD, Australia.

Abstract

Spray drift is an inescapable consequence of agricultural plant protection operation, which has always been one of the major concerns in the spray application industry. Spray drift evaluation is essential to provide a basis for the rational selection of spray technique and working surroundings. Nowadays, conventional sampling methods with passive collectors used in drift evaluation are complex, time-consuming, and labor-intensive. The aim of this paper is to present a method to evaluate spray drift based on 3D LiDAR sensor and to test the feasibility of alternatives to passive collectors. Firstly, a drift measurement algorithm was established based on point clouds data of 3D LiDAR. Wind tunnel tests included three types of agricultural nozzles, three pressure settings, and five wind speed settings were conducted. LiDAR sensor and passive collectors (polyethylene lines) were placed downwind from the nozzle to measure drift droplets in a vertical plane. Drift deposition volume on each line and the number of LiDAR droplet points in the corresponding height of the collecting line were calculated, and the influencing factors of this new method were analyzed. The results show that 3D LiDAR measurements provide a rich spatial information, such as the height and width of the drift droplet distribution, etc. High coefficients of determination (R ² > 0.75) were observed for drift points measured by 3D LiDAR compared to the deposition volume captured by passive collectors, and the anti-drift IDK12002 nozzle at 0.2 MPa spray pressure has the largest R ² value, which is 0.9583. Drift assessment with 3D LiDAR is sensitive to droplet density or drift mass in space and nozzle initial droplet spectrum; in general, larger droplet density or drift mass and smaller droplet size are not conducive to LiDAR detection, while the appropriate threshold range still needs further study. This study demonstrates that 3D LiDAR has the potential to be used as an alternative tool for rapid assessment of spray drift.

Keywords: 3D LiDAR; droplet; plant protection; point clouds; remote sensing; spray drift.