Optimization of variables for maximizing efficacy and efficiency in aerial spray application to cotton using unmanned aerial systems

Juan Liao, Ying Zang, Xiwen Luo, Zhiyan Zhou, Yubin Lan, Yu Zang, Xiuyan Gu, Weiming Xu, Andrew John Hewitt

Abstract


Aerial spraying can support efficient defoliation without crop contact. With the recent introduction to unmanned aerial system (UAS) for aerial spraying in China, there is a need to determine the optimum application variables to achieve high efficacy and efficiency with low costs. The present research involved field studies across two annual cotton production seasons in North Xinjiang, China. Four factors, including volume rate (A), tank mix including spray adjuvants (B), flight altitude (C), flight speed (D) and three levels of L9 (34) orthogonal arrays were carried out to optimize the application parameters for three types of UASs. These included different numbers of rotors as follows: four-rotors, six-rotors and eight-rotors. Spray coverage, distribution uniformity (coefficient of variation (CV) of droplet coverage), rates of cotton defoliation and boll opening, application efficiency and cost were measured and assessed. Results showed that: (1) the rates of defoliation and boll opening by aerial cotton defoliant application could meet the requirement of cotton mechanized harvesting; (2) the optimal scenario for the three UASs was A3B2C1D3, Volume rate (A3): 48 L/hm2; Tank mix and concentration (B2): (Tuotulong 225 + Sujie 750 + Ethephon 2250) mL/hm2, Flight altitude (C1): 1.5 m, and Flight speeds (D3) for unmanned helicopters with four-rotors, six-rotors and eight-rotors were 3.12 m/s, 2.51 m/s and 3.76 m/s, respectively. These results can provide guidance for cotton defoliant aerial spraying in China using UAS.
Keywords: unmanned aerial system (UAS), unmanned aerial vehicle (UAV), unmanned electric helicopter (UEH), cotton defoliant, aerial spraying, parameter optimization
DOI: 10.25165/j.ijabe.20191202.4288

Citation: Liao J, Zang Y, Luo X W, Zhou Z Y, Lan Y B, Zang Y, et al. Optimization of variables for maximizing efficacy and efficiency in aerial spray application to cotton using unmanned aerial systems. Int J Agric & Biol Eng, 2019; 12(2): 10–17.

Keywords


unmanned aerial system (UAS), unmanned aerial vehicle (UAV), unmanned electric helicopter (UEH), cotton defoliant, aerial spraying, parameter optimization

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References


Liu H L. The reason for decreasing of cotton plant positivity. China Cooperation Times, 2013; 4: 1–2. (in Chinese)

Gilbert C. Mechanization of Cotton Production since World War II. Agricultural History, 1980; 54(1):190-207.

Hopkins A R, Taft H M. Control of cotton pests by aerial application of ultra-low-volume (undiluted) technical insecticides. Journal of Economic Entomology, 1967; 60(60): 561–565.

Chester G, Ward R J. Occupational exposure and drift hazard during aerial application of paraquat to cotton. Arch Environ Contam Toxicol, 1984; 13(5): 551–563.

Martin D E, Latheef M A. Aerial electrostatic spray deposition and canopy penetration in cotton. Journal of Electrostatics, 2017; 90: 38–44.

Johnstone D R, Johnstone K A. Aerial Spraying of Cotton in Swaziland. Pans Pest Articles & News Summaries, 1977; 23(1): 13–26.

Nicholls J W, Dorr G J, Woods N. Improving the ground and aerial application of pesticides in cotton: an Australian experience. Aspects of Applied Biology, 2004; 71: 509–515.

Favoreto L, Faleiro V O, Freitas M A, Brauwers L R, Rafael G, Homiak J A, et al. First report of Aphelenchoides besseyi infecting aerial part of cotton plants in Brazil. Plant Disease, 2018.

Xin F, Zhao J, Zhou Y, Wang G, Han X, Fu W, et al. Effects of dosage and spraying volume on cotton defoliants efficacy: a case study based on application of unmanned aerial vehicles. Agronomy, 2018; 85(8): 1–15.

He X, Bonds J, Herbst A, Langenakens, J. Recent development of unmanned aerial vehicle for plant protection in East Asia. Int J Agric & Biol Eng, 2017; 10(3): 18–30.

Stevens M N, Coloe B, Atkins E M. Platform-Independent geofencing for low altitude UAS operations: 15th AIAA Aviation Technology, Integration, and Operations Conference, Dallas, 2015.

Wang S L, Song J L, He X K, Song L, Wang X N, Wang C L, et al. Performances evaluation of four typical unmanned aerial vehicles used for pesticide application in China. Int J Agric & Biol Eng, 2017; 10(4): 22–31.

Czaczyk Z, Fritz B K, Hoffmann W C, Majewski S. Settings parameters for aerial pesticides application using Gyroplane: IX International Scientific Symposium on Farm Machinery and Processes Management in Sustainable Agriculture, Lublin, Poland, 2017.

Lefebvre A H, McDonell V G. Atomization and sprays. Boca Raton, Florida: CRC Press, 2017.

Gil E, Gallart M, Balsari P, Marucco P, Almajano M P, Llop J. Influence of wind velocity and wind direction on measurements of spray drift potential of boom sprayers using drift test bench. Agricultural and Forest Meteorology, 2015; 202: 94–101.

Hewitt A J. Droplet size spectra classification categories in aerial application scenarios. Crop Protection, 2008; 27(9): 1284–1288.

Zhang J, He X K, Song J L, Zeng A J, Liu Y J, Li X F. Influence of spraying parameters of unmanned aircraft on droplets deposition. Transactions of the CSAM, 2011; 43(12): 94–96. (in Chinese)

Zhang D Y, Lan Y B, Chen L P, Hoffmann C W, Zhang R R, Xu G, et al. Measurement and analysis of aviation spraying key parameters for M-18B Dromader and Thrush 510G aircraft. 2014 ASABE and CSBE/SCGAB Annual International Meeting, Montreal, Quebec Canada, 2014.

Chen S D, Lan Y B, Li J Y, Zhou Z Y, Liu A M. Effect of wind field below unmanned helicopter on droplet deposition distribution of aerial spraying. Int J Agric & Biol Eng, 2017; 10(3): 67–77.

Hewitt A J, Johnson D R, Fish J D, Hermansky C G, Valcore D L. Development of the spray drift task force database for aerial application. Environmental Toxicology and Chemistry, 2002; 21(3): 648–658.

Fritz B K. Meteorological effects on deposition and drift of aerially applied sprays. Transactions of the ASABE, 2006; 49(5): 1295–1301.

Brain R A, Perine J, Cooke C, Ellis C B, Harrington P, Lane A, et al. Evaluating the effects of herbicide drift on nontarget terrestrial plants: A case study with mesotrione. Environmental Toxicology and Chemistry, 2017; 36(9): 2465–2475.

Cerruto E, Failla S, Longo D, Longo D, Manetto G. Simulation of water sensitive papers for spray analysis. Agric Eng Int: CIGR Journal, 2016; 18(4): 22–29.

Qin W C, Qiu B J, Xue X Y, Chen C, Xu Z F, Zhou Q Q. Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Crop Protection, 2016; 85: 79–88.

Cao Y, Yan Y P, Feng Z X, Zhang Y, Zhu B, Guo H L, Fang R. Cotton defoliant application test and methods analysis of efficacy improvement. Crops, 2012; 4: 144–147.

Spina D, Ahluwalia A, Spina D, Ahluwalia A, Alexander S P A, Giembycz M A, et al. Experimental design and analysis and their reporting: new guidance for publication in BJP. British Journal of Pharmacology, 2015; 172(14): 3461–3471.

Pang S, Wang J, Wang X, Wang X L. Application of orthogonal array and walsh transform in resilient function. Chinese Journal of Electronics, 2018; 27(2): 281–286.

X Gang, Chen L P, Zhang R R. An image processing system for evaluation of aerial application quality. Proceedings of the 2016 International Conference on Intelligent Information Processing, Wuhan, China, 2016.

Ferguson J C, Chechetto R G, O Donnell C C, Fritz B K, Hoffmann W C, Coleman C E, et al. Assessing a novel smartphone application–SnapCard, compared to five imaging systems to quantify droplet deposition on artificial collectors. Computers and Electronics in Agriculture, 2016; 128: 193–198.

Carroll J H. The effects of sprayer speed and droplet size on herbicide burndown efficacy. University of Arkansas, Arkansas, US, 2017.

Fritz B K, Hoffmann W C, Czaczyk Z, Bagley W, Henry R. Measuring droplet size of agricultural spray nozzles-measurement distance and airspeed effects. Journal of Plant Protection Research, 2012; 52(4): 447–457.

ANSI/ASAE S572.1. Spray nozzle classification by drop spectra. ASABE Standards, St. Joseph, MI, USA, 2009.

MHT Standards 1040-2011: Determining application rates and distribution patterns from aerial application equipment, Beijing: MHT, 2011.

Shan Z C. The gray correlation analysis of agricultural products logistics and its influence factors. Systems Engineering, 2012; 10: 19.

Yuan F, Zhan Y, Wang Y. Data density correlation degree clustering method for data aggregation in WSN. IEEE Sensors Journal, 2014; 14(4): 1089–1098.




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