PMID- 38340824
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240221
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 918
DP  - 2024 Mar 25
TI  - Toward a remote sensing method based on commercial LiDAR sensors for the 
      measurement of spray drift and potential drift reduction.
PG  - 170819
LID - S0048-9697(24)00958-6 [pii]
LID - 10.1016/j.scitotenv.2024.170819 [doi]
AB  - 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 labor-intensive 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 mum 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.
CI  - Copyright (c) 2024 Elsevier B.V. All rights reserved.
FAU - Li, Longlong
AU  - Li L
AD  - Research Center of Intelligent Equipment, Beijing Academy of Agriculture and 
      Forestry Sciences, Beijing 100097, China; National Center for International 
      Research on Agricultural Aerial Application Technology, Beijing 100097, China.
FAU - Zhang, Ruirui
AU  - Zhang R
AD  - Research Center of Intelligent Equipment, Beijing Academy of Agriculture and 
      Forestry Sciences, Beijing 100097, China; National Center for International 
      Research on Agricultural Aerial Application Technology, Beijing 100097, China. 
      Electronic address: zhangrr@nercita.org.cn.
FAU - Chen, Liping
AU  - Chen L
AD  - Research Center of Intelligent Equipment, Beijing Academy of Agriculture and 
      Forestry Sciences, Beijing 100097, China; National Center for International 
      Research on Agricultural Aerial Application Technology, Beijing 100097, China.
FAU - Hewitt, Andrew J
AU  - Hewitt AJ
AD  - Centre for Pesticide Application and Safety, The University of Queensland, 
      Queensland 4072, Australia.
FAU - He, Xiongkui
AU  - He X
AD  - Centre for Chemicals Application Technology, China Agricultural University, 
      Beijing 100193, China.
FAU - Ding, Chenchen
AU  - Ding C
AD  - Research Center of Intelligent Equipment, Beijing Academy of Agriculture and 
      Forestry Sciences, Beijing 100097, China; National Center for International 
      Research on Agricultural Aerial Application Technology, Beijing 100097, China.
FAU - Tang, Qing
AU  - Tang Q
AD  - Research Center of Intelligent Equipment, Beijing Academy of Agriculture and 
      Forestry Sciences, Beijing 100097, China; National Center for International 
      Research on Agricultural Aerial Application Technology, Beijing 100097, China.
FAU - Liu, Boqin
AU  - Liu B
AD  - Research Center of Intelligent Equipment, Beijing Academy of Agriculture and 
      Forestry Sciences, Beijing 100097, China; National Center for International 
      Research on Agricultural Aerial Application Technology, Beijing 100097, China.
LA  - eng
PT  - Journal Article
DEP - 20240209
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
OTO - NOTNLM
OT  - Drift reduction percentage
OT  - LiDAR
OT  - Nozzles
OT  - Point clouds
OT  - Spray drift
OT  - Sprayers
COIS- Declaration of competing interest The authors declare that they have no competing 
      financial interests or personal relationships that may have influenced the work 
      reported in this study.
EDAT- 2024/02/11 07:42
MHDA- 2024/02/11 07:43
CRDT- 2024/02/10 19:13
PHST- 2023/09/21 00:00 [received]
PHST- 2024/02/05 00:00 [revised]
PHST- 2024/02/06 00:00 [accepted]
PHST- 2024/02/11 07:43 [medline]
PHST- 2024/02/11 07:42 [pubmed]
PHST- 2024/02/10 19:13 [entrez]
AID - S0048-9697(24)00958-6 [pii]
AID - 10.1016/j.scitotenv.2024.170819 [doi]
PST - ppublish
SO  - Sci Total Environ. 2024 Mar 25;918:170819. doi: 10.1016/j.scitotenv.2024.170819. 
      Epub 2024 Feb 9.