Literature DB >> 33689892

Assessment of spray deposition, drift and mass balance from unmanned aerial vehicle sprayer using an artificial vineyard.

Changling Wang1, Andreas Herbst2, Aijun Zeng3, Supakorn Wongsuk3, Baiyu Qiao3, Peng Qi3, Jane Bonds4, Verena Overbeck2, Yi Yang5, Wanlin Gao6, Xiongkui He7.   

Abstract

Under the rapid development of unmanned aerial vehicle (UAV) plant protection products (PPP) application in Asian countries, the drift risk of UAV sprayer operation in orchard or vineyard is fairly high because of the much finer droplets generated and the higher height than ground sprayers, increasing threats to non-targeted crop, human and environment. However, there is few of comprehensive experimental study on the effects of UAV type and nozzle type on spray deposition and drift from UAV sprayer. The objectives of this study were to compare the spray performance of three different typical commercial UAV types (helicopter, 6-rotor and 8-rotor) with two nozzles types (hollow cone nozzle, HCN and air-injector flat fan nozzle, AIN) in vineyard. An artificial vineyard and three vertical collection frames, designed and built by ourselves, were applied for collecting droplets together with PVC collectors, petri dishes and rotary samples. The characteristics of deposition, drift and mass balance of UAV aerial spraying in vineyard were analyzed. As a result, under the crosswind speed of 3.11-3.79 m/s, AIN promoted spray deposition and uniformity and reduced drift significantly compared to HCN for all tested UAVs, improving of the utilization of PPP. The fitted regression functions of the sedimenting and airborne drift were obtained, respectively, and the drift percentage reduction values of AIN compared to HCN determined based on those functions varied from 81% to 95%. With HCN, 49.3%-73.4% of measured droplets drifted into non-targeted area and the highest proportion of drift loss was found for the airborne spray drift. According to the principle of more deposition and less drift, the spray performance of the three UAVs can be ranked in an order of 6-rotor, 8-rotor and helicopter, and two main reasons causing the difference in spray performance were the vortex airflow and the nozzle arrangement.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Airborne drift; Deposition; Droplet; Sedimenting drift; Sprayer; Unmanned aerial vehicle

Year:  2021        PMID: 33689892     DOI: 10.1016/j.scitotenv.2021.146181

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  5 in total

Review 1.  Characteristics of unmanned aerial spraying systems and related spray drift: A review.

Authors:  Pengchao Chen; Jean Paul Douzals; Yubin Lan; Eric Cotteux; Xavier Delpuech; Guilhem Pouxviel; Yilong Zhan
Journal:  Front Plant Sci       Date:  2022-08-08       Impact factor: 6.627

Review 2.  Research on Methods Decreasing Pesticide Waste Based on Plant Protection Unmanned Aerial Vehicles: A Review.

Authors:  Heming Hu; Yutaka Kaizu; Jingjing Huang; Kenichi Furuhashi; Hongduo Zhang; Ming Li; Kenji Imou
Journal:  Front Plant Sci       Date:  2022-07-07       Impact factor: 6.627

3.  Comprehensive assessment of intelligent unmanned vehicle techniques in pesticide application: A case study in pear orchard.

Authors:  Yulin Jiang; Xiongkui He; Jianli Song; Yajia Liu; Changling Wang; Tian Li; Peng Qi; Congwei Yu; Fu Chen
Journal:  Front Plant Sci       Date:  2022-08-23       Impact factor: 6.627

4.  Effect of flight velocity on droplet deposition and drift of combined pesticides sprayed using an unmanned aerial vehicle sprayer in a peach orchard.

Authors:  Longlong Li; Zhihong Hu; Qingju Liu; Tongchuan Yi; Ping Han; Ruirui Zhang; Ligang Pan
Journal:  Front Plant Sci       Date:  2022-09-29       Impact factor: 6.627

5.  A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.

Authors:  Yangyang Liu; Pengyang Zhang; Yu Ru; Delin Wu; Shunli Wang; Niuniu Yin; Fansheng Meng; Zhongcheng Liu
Journal:  Front Plant Sci       Date:  2022-09-16       Impact factor: 6.627

  5 in total

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