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Toward a remote sensing method based on commercial LiDAR sensors for the measurement of spray drift and potential drift reduction

文献类型： 外文期刊

作者： Li, Longlong 1 ; Zhang, Ruirui 1 ; Chen, Liping 1 ; Hewitt, Andrew J. 3 ; He, Xiongkui 4 ; Ding, Chenchen 1 ; Tang, Qing 1 ; Liu, Boqin 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.Natl Ctr Int Res Agr Aerial Applicat Technol, Beijing 100097, Peoples R China

3.Univ Queensland, Ctr Pesticide Applicat & Safety, St Lucia, Qld 4072, Australia

4.China Agr Univ, Ctr Chem Applicat Technol, Beijing 100193, Peoples R China

关键词： LiDAR; Point clouds; Spray drift; Drift reduction percentage; Nozzles; Sprayers

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：9.8； 五年影响因子：9.6 ）

ISSN： 0048-9697

年卷期： 2024 年 918 卷

页码：

收录情况： SCI

摘要： Spray drift is inevitable in chemical applications, drawing global attention because of its potential environmental pollution and the risk of exposing bystanders to pesticides. This issue has become more pronounced with a growing consensus on the need for enhanced environmental safeguards in agricultural practices. Traditionally, spray drift measurements, crucial for refining spray techniques, relied on intricate, time-consuming, and laborintensive sampling methods utilizing passive collectors. In this study, we investigated the feasibility of using close-range remote sensing technology based on Light Detection and Ranging (LiDAR) point clouds to implement drift measurements and drift reduction classification. The results show that LiDAR-based point clouds vividly depict the spatial dispersion and movement of droplets within the vertical plane. The capability of LiDAR to accurately determine drift deposition was demonstrated, evident from the high R-2 values of 0.847, 0.748 and 0.860 achieved for indoor, wind tunnel and field environments, respectively. Droplets smaller than 100 mu m and with a density below 50 deposits.cm(-2).s(-1) posed challenges for LiDAR detection. To address these challenges, the use of multichannel LiDAR with higher wavelengths presents a potential solution, warranting further exploration. Furthermore, we found a satisfactory consistency when comparing the drift reduction classification calculated from LiDAR measurements with those obtained though passive collectors, both in indoor tests and the unmanned air-assisted sprayer (UAAS) field test. However, in environments with less dense clouds of larger droplets, a contradiction emerged between higher drift deposition and lower scanned droplet counts, potentially leading to deviations in the calculated drift potential reduction percentage (DPRP). This was exemplified in a field test using an unmanned aerial vehicle sprayer (UAVS). Our findings provide valuable insights into the monitoring and quantification of pesticide drift at close range using LiDAR technology, paving the way for more precise and efficient drift assessment methodologies.

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