Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-02-01T04:06:05.190Z Has data issue: false hasContentIssue false
  • English
  • Français

Coverage and drift potential associated with nozzle and speed selection for herbicide applications using an unmanned aerial sprayer

Published online by Cambridge University Press:  09 October 2019

Joseph E. Hunter III ,
Travis W. Gannon ,
Robert J. Richardson ,
Fred H. Yelverton  and
Ramon G. Leon Open the ORCID record for Ramon G. Leon [Opens in a new window]
Joseph E. Hunter III
Affiliation:
Graduate Student, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, USA
Travis W. Gannon
Affiliation:
Associate Professor, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, USA
Robert J. Richardson
Affiliation:
Professor, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, USA
Fred H. Yelverton
Affiliation:
Professor, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, USA
Ramon G. Leon*
Affiliation:
Assistant Professor, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, USA
*
Author for correspondence: Ramon G. Leon, Assistant Professor, 4402C Williams Hall, North Carolina State University, Raleigh, NC 27695. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In recent years, unmanned aerial vehicle (UAV) technology has expanded to include UAV sprayers capable of applying pesticides. Very little research has been conducted to optimize application parameters and measure the potential of off-target movement from UAV-based pesticide applications. Field experiments were conducted in Raleigh, NC during spring 2018 to characterize the effect of different application speeds and nozzle types on target area coverage and uniformity of UAV applications. The highest coverage was achieved with an application speed of 1 m s−1 and ranged from 30% to 60%, whereas applications at 7 m s−1 yielded 13% to 22% coverage. Coverage consistently decreased as application speed increased across all nozzles, with extended-range flat-spray nozzles declining at a faster rate than air-induction nozzles, likely due to higher drift. Experiments measuring the drift potential of UAV-applied pesticides using extended-range flat spray, air-induction flat-spray, turbo air–induction flat-spray, and hollow-cone nozzles under 0, 2, 4, 7, and 9 m s−1 perpendicular wind conditions in the immediate 1.75 m above the target were conducted in the absence of natural wind. Off-target movement was observed under all perpendicular wind conditions with all nozzles tested but was nondetectable beyond 5 m away from the target. Coverage from all nozzles exhibited a concave-shaped curve in response to the increasing perpendicular wind speed due to turbulence. The maximum target coverage in drift studies was observed when the perpendicular wind was 0 and 8.94 m s−1, but higher turbulence at the two highest perpendicular wind speeds (6.71 and 8.94 m s−1) increased coverage variability, whereas the lowest variability was observed at 2.24 m s−1 wind speed. Results suggested that air-induction flat-spray and turbo air–induction flat-spray nozzles and an application speed of 3 m s−1 provided an adequate coverage of target areas while minimizing off-target movement risk.

Keywords

Bradley Hanson, University of California, DavisUAVprecision agriculturesite-specificoff-targetdropletpesticide application
Type
Research Article
Information
Weed Technology , Volume 34 , Issue 2 , April 2020 , pp. 235 - 240
Copyright
© Weed Science Society of America, 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bird, SL, Esterly, DM, Perry, SG (1996) Off-target deposition of pesticides from agricultural aerial spray applications. J Environ Qual 25:1095–1104CrossRefGoogle Scholar
Blyenburgh, P (1999) UAVs: an overview. Air Space Eur 1:43–47CrossRefGoogle Scholar
Bode, LE, Butler, BJ, Goering, CE (1976) Spray drift and recovery as affected by spray thickener, nozzle type, and nozzle pressure. Trans ASAE 19:213–218CrossRefGoogle Scholar
Bouse, LF, Kirk, IW, Bode, LE (1990) Effect of spray mixture on droplet size. Trans ASAE 33:783–788CrossRefGoogle Scholar
Bretthauer, S (2015) Aerial applications in the USA. Outlook Pest Mgmt 26:192–198CrossRefGoogle Scholar
Brown, CR, Giles, DK (2018) Measurement of pesticide drift from unmanned aerial vehicle application to a vineyard. Trans ASABE 61:1539–1546CrossRefGoogle Scholar
Castaldi, F, Pelosi, F, Pascucci, S, Casa, R (2017) Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precis Agric 18:76–94CrossRefGoogle Scholar
Christensen, S, Søgaard, HT, Kudsk, P, Nørremark, M, Lund, I, Nadimi, ES, Jørgensen, R (2009) Site-specific weed control technologies. Weed Res 49:233–241CrossRefGoogle Scholar
Combellack, JH, Western, NM, Richardson, RG (1996) A comparison of the drift potential of a novel twin fluid nozzle with conventional low volume flat fan nozzles when using a range of adjuvants. Crop Prot 15:147–152CrossRefGoogle Scholar
Creech, CF, Henry, RS, Fritz, BK, Kruger, GR (2015) Influence of herbicide active ingredient, nozzle type, orifice size, spray pressure, and carrier volume rate on spray droplet size characteristics. Weed Technol 29:298–310CrossRefGoogle Scholar
Deng, L, Mao, Z, Xiaojuan, H, Duan, F, Yan, Y (2018) UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS J Photogramm 146:124–136CrossRefGoogle Scholar
Faical, BS, Freitas, H, Gomes, PH, Mano, LY, Pessin, G, Carvalho, AC, Krishnamachari, B, Ueyama, J (2017) An adaptive approach for UAV-based pesticide spraying in dynamic environments. Comput Electron Agr 138:210–223CrossRefGoogle Scholar
Fengbo, Y, Xinyu, X, Zhang, L, Zhu, S (2017) Numerical simulation and experimental verification on downwash air flow of six-rotor agricultural unmanned aerial vehicle in hover. Int J Agric Biol Eng 10:41–53Google Scholar
Gerhards, R, Oebel, H (2006) Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res 46:185–193CrossRefGoogle Scholar
Giles, DK (2016) Use of remotely piloted aircraft for pesticide applications. Outlook Pest Mgmt 27:213–216CrossRefGoogle Scholar
Giles, DK, Billing, R, Singh, W (2016) Performance results, economic viability and outlook for remotely piloted aircraft for agricultural spraying. Asp Appl Biol 132:15–21Google Scholar
Grover, R, Maybank, J, Caldwell, BC, Wolf, TM (1997) Airborne off-target losses and deposition characteristics from a self-propelled, high speed, and high clearance ground sprayer. Can J Plant Sci 77:493–500CrossRefGoogle Scholar
He, X (2018) Rapid development of unmanned aerial vehicles (UAV) for plant protection and application technology in China. Outlook Pest Mgmt 29:162–167CrossRefGoogle Scholar
Hewitt, AJ, Solomon, KR, Marshall, EJ (2009) Spray droplet size, drift potential, and risks to nontarget organisms from aerially applied glyphosate for coca control in Colombia. J Toxicol Environ Health Part A 72:921–929CrossRefGoogle ScholarPubMed
Huang, Y, Hoffmann, C, Fritz, B, Lan, Y (2008) Development of an Unmanned Aerial Vehicle-Based Spray System for Highly Accurate Site-Specific Application. Paper 083909. 2008 ASABE Annual International Meeting, Providence, Rhode Island, June 29–July 2, 2008.Google Scholar
Huang, YB, Thomson, SJ, Hoffmann, WC, Lan, YB, Fritz, BK (2013) Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int J Agric Biol Eng 6:1–10Google Scholar
Knoche, M (1994) Effect of droplet size and carrier volume on performance of foliage-applied herbicides. Crop Prot 13:163–178CrossRefGoogle Scholar
Lan, Y, Shengde, C, Fritz, B (2017) Current status and future trends of precision agricultural aviation technologies. Int J Agric Biol Eng 10:1–17Google Scholar
Legleiter, TR, Johnson, WG (2016) Herbicide coverage in narrow row soybean as influenced by spray nozzle design and carrier volume. Crop Prot 83:1–8CrossRefGoogle Scholar
Mannarino, AJ, Langley, RL, Kearney, GD (2017) Factors associated with aerial pesticide applicator crashes in the United States: 1995–2013. Glob Environ Health Saf 1(2:14):17Google Scholar
Meng, YH, Lan, YB, Mei, GY, Guo, YW, Song, JL, Wang, ZG (2018) Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle for wheat aphids control. Int J Agric Biol Eng 11:46–53Google Scholar
Nansen, C, Ferguson, JC, Moore, J, Groves, L, Emery, R, Garel, N, Hewitt, A (2015) Optimizing pesticide spray coverage using a novel web and smartphone too, SnapCard. Agron Sustain Dev 35:1075–1085Google Scholar
San Martín, C, Andújar, D, Barroso, J, Fernández-Quintanilla, C, Dorado, J (2016) Weed decision threshold as a key factor for herbicide reductions in site specific weed management. Weed Technol 30:888–897CrossRefGoogle Scholar
Teske, ME, Wachspress, DA, Thistle, HW (2018) Prediction of aerial spray release from UAVs. 2018 Trans ASABE 61:909–918CrossRefGoogle Scholar
Wiles, LJ (2009) Beyond patch spraying: site-specific weed management with several herbicides. Precis Agric 10:277–290CrossRefGoogle Scholar
Xiongkui, H, Bonds, J, Herbst, A, Langenakens, J (2017) Recent development of unmanned aerial vehicle for plant protection in East Asia. Int J Agric Biol Eng 10:18–30Google Scholar
Xue, X, Lan, Y, Zhu, S, Chun, C, Hoffman, WC (2016) Develop an unmanned aerial vehicle based automatic aerial spraying system. Comput Electron Agr 128:58–66CrossRefGoogle Scholar
Yang, F, Xue, X, Cia, C, Sun, Z, Zhou, Q (2018) Numerical simulation and analysis on spray drift movement of multirotor plant protection unmanned aerial vehicle. Energies 11:2399CrossRefGoogle Scholar
Yongjun, Z, Shenghui, Y, Chunjiang, Z, Liping, C, Lan, Y, Yu, T (2017) Modelling operation parameters of UAV on spray effects at different growth stages of corn. Int J Agric Biol Eng 10:57–66Google Scholar
Zhang, C, Kovacs, JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13:693–671 CrossRefGoogle Scholar