Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV)

Haibo Chen, Yubin Lan, Bradley K Fritz, W. Clint Hoffmann, Shengbo Liu

Abstract


With changing climate and farmland ecological conditions, pest outbreaks in agricultural landscapes are becoming more frequent, increasing the need for improved crop production tools and methods. UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products. This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection, and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals. Additionally, the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed, and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing, variable-rate technologies, and spray drift models are considered. Based on the reviewed literature, future developments and the impacts of these UAV technologies are projected. This review may inspire the innovation of the combined use of big data analytics and UAV technology, precision agricultural spraying technology, drift reduction technology, swarm UAV cooperative technology, and other supporting technologies for UAV-based aerial spraying for scientific research in the world.
Keywords: UAV, plant protection, spraying technology, drift reduction, pesticide efficacy, spraying model, big data analytics
DOI: 10.25165/j.ijabe.20211401.5714

Citation: Chen H B, Lan Y B, Fritz B K, Hoffmann W C, Liu S B. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). Int J Agric & Biol Eng, 2021; 14(1): 38–49.

Keywords


UAV, plant protection, spraying technology, drift reduction, pesticide efficacy, spraying model, big data analytics

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References


Hunter M C, Smith R G, Schipanski M E, Atwood L W, Mortensen D A. Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience, 2017; 67(4): 386–391.

Yuan L, Bao Z Y, Zhang H B, Zhang Y T, Liang X. Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery. Optik, 2017; 145: 66–73.

Macfadyen S, McDonald G, Hill M P. From species distributions to climate change adaptation: knowledge gaps in managing invertebrate pests in broad-acre grain crops. Agric Ecosyst Environ, 2018; 253: 208–219.

Lan Y B, Chen S D. Current status and trends of plant protection UAV and its spraying technology in China. Int J Precisi Agric Aviat, 2018; 1(1): 1–9.

Hilz E, Vermeer A W P. Spray drift review: the extent to which a formulation can contribute to spray drift reduction. Crop Prot, 2013; 44: 75–83.

Oerke E C. Crop losses to pests. J Agric Sci, 2006; 144(1): 31–43.

Lan Y B, Chen S D, Fritz B K. Current status and future trends of precision agricultural aviation technologies. Int J Agric & Biol Eng, 2017; 10(3): 1–17.

He X K, 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.

Mogili U R, Deepak B B V L. Review on application of drone systems in precision agriculture. Procedia Comput. Sci, 2018; 133: 502–509.

Huang Y B, Thomson S J, Hoffmann W C, Lan Y B, Fritz B K. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int J Agric & Biol Eng, 2013; 6(3): 1–10.

Liao J, Zang Y, Zhou Z Y, Luo X W. Quality evaluation method and optimization of operating parameters in crop aerial spraying technology. Transactions of the CSAE, 2015; 31(Supp.2): 38–46. (in Chinese)

Chen S D, Lan Y B, Li J Y, Xu X J, Wang Z G, Peng B. Evaluation and test of effective spraying width of aerial spraying on plant protection UAV. Transactions of the CSAE, 2017a; 33(7): 82–90. (in Chinese)

Sarri D, Martelloni L, Rimediotti M, Lisci R, Lombardo S, Vieri M. Testing a multi-rotor unmanned aerial vehicle for spray application in high slope terraced vineyard. J Agric Eng, 2019; 50(1): 38–47.

Fritz B K. Role of atmospheric stability in drift and deposition of aerially applied sprays–preliminary results. 2004 ASAE Annual Meeting, Ottawa, CA, 2004; 58:742–755.

Kirk I W, Fritz B K, Hoffmann W C. Aerial methods for increasing spray deposits on wheat heads. 2004 ASAE Annual Meeting, Ottawa, CA, 2004; 58:716–729.

Xue X Y, Tu K, Lan Y B, Qin W C, Zhang L. Effects of pesticides aerial applications on rice quality. Transactions of the CSAM, 2013; 44(12): 94–79. (in Chinese)

Qin W C, Xue X Y, Zhou L X, Zhang S C, Sun Z, Kong W, et al. Effects of spraying parameters of unmanned aerial vehicle on droplets deposition distribution of maize canopies. Transactions of the CSAE, 2014; 30(5): 50–56. (in Chinese)

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.

Lou Z X, Xin F, Han X Q, Lan Y B, Duan T Z, Fu W. Effect of unmanned aerial vehicle flight height on droplet distribution, drift and control of cotton aphids and spider mites. Agronomy, 2018; 8(9): 187. doi: 10.3390/argonomy8090187.

Richardson B, Rolando C A, Kimberley M O, Strand T M. Spray application efficiency from a multi-rotor unmanned aerial vehicle configured for aerial pesticide application. Trans ASABE, 2019; 62(6): 1447–1453.

Chen H B, Fritz K B, Lan Y B, Zhou Z Y, Zheng J F. An overview of spray nozzles for plant protection from manned aircrafts: Present research and prospective. Int J Precis Agric Aviat, 2020; 3(2): 1–12.

Zhang X Q, Liang Y J, Qin Z Q, Li D W, Wei C Y, Wei J J, et al. Application of multi-rotor unmanned aerial vehicle application in management of stem borer (Lepidoptera) in sugarcane. Sugar Tech, 2019; 21(5): 847–852.

Li H, He Y J, Qin C B, Liu D Q, Zhang K F. Ecological analysis on spray performance of multi-rotor unmanned aerial sprayer in soybean field. Ekoloji, 2019; 28(107): 4573–4579.

Giles D K. Use of remotely piloted aircraft for pesticide applications. Outlook Pest Mgmt, 2016; 27(5): 213–216.

Zhu H, Lan Y B, Wu W F, Hoffmann W C, Huang Y B, Xue X Y, et al. Development of a PWM precision spraying controller for UAV. J Bionic Eng, 2010; 7(3): 276–283.

Xu B, Chen L P, Tan Y, Xu M. Path planning based on minimum energy consumption for plant protection UAVs in sorties. Trans CSAM 2015; 46(11): 36–42. (in Chinese)

Xue X Y, Lan Y B, Sun Z, Chang C, Hoffmann W C. Develop an unmanned aerial vehicle based automatic aerial spraying system. Comput Electron Agric, 2016; 128: 58–66.

Wang Y, Chen H T, Li H C. 3D path planning approach based on gravitational search algorithm for sprayer UAV. Transactions of the CSAM, 2018; 49(2): 28–33. (in Chinese)

Fritz B K, Hoffmann W C, Lan Y B. Evaluation of the EPA drift reduction technology (DRT) low-speed wind tunnel protocol. J ASTM Int, 2009; 6(4): 12.

Thomson S J, Womac A R, Mulrooney J E. Reducing pesticide drift by considering propeller rotation effects from aerial application near buffer zones. Sustain Agric Res, 2013; 2(3): 41–51.

Yang Z L, Qi L J, Wu Y L. Influence of UAV rotor down-wash airflow for droplet penetration. 2018 ASAE Annual Meeting, Michigan, USA, 2018.

Zhou Y E, Xu G H, Shi Y J. High-resolution numerical research on formation and evolution mechanism of rotor blade tip vortex. Acta Aeronaut. Astronaut. Sin., 2017; 38(7): 43–53. (in Chinese)

Li J Y, Lan Y B, Shi Y Y. Research progress on airflow characteristics and field pesticide application system of rotary-wing UAV. Trans CSAE, 2018; 34(12): 104–118. (in Chinese)

Wang J F, Xu W B, Wen J L, Wang X Y, Luo B T. Numerical simulation on gas-liquid phase flow of large-scale plant protection unmanned aerial vehicle spraying. Trans CSAM, 2017; 48(9): 62–69. (in Chinese)

Xu W B, Wang J F, Wen J L, Wang X Y. Numerical simulation for downwash flow field of large-size plant protection unmanned helicopter hedgehopping. Journal of Jiangsu University, 2017; 38(6): 665–671. (in Chinese)

Zhang S C, Xue X Y, Sun Z, Zhou L X, Jin Y K. Downwash distribution of single-rotor unmanned agricultural helicopter on hovering state. Int J Agric & Biol Eng, 2017; 10(5): 14–24.

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

Yang F B, Xue X Y, Zhang L, Sun Z. Numerical simulation and experimental verification on downwash air flow of six-rotor agricultural unmanned aerial vehicle in hover. Int J Agric & Biol Eng, 2017; 10(4): 41–53.

Tan F, Lian Q, Liu C L, Jin B K. Measurement of downwash velocity generated by rotors of agriculture drones. INMATEH- Agric Eng, 2018; 55(2): 141–150.

Xue X Y, Tu K, Qin W C, Lan Y B, Zhang H H. Drift and deposition of ultra-low altitude and low volume application in paddy field. Int J Agric & Biol Eng, 2014; 7(4): 23–28.

Chen S D, Lan Y B, Li J Y, Zhou Z Y, Liu A M, Xu X J. Comparison of the pesticide effects of aerial and artificial spray applications for rice. Journal of South China Agricultural University, 2017c; 38(4): 103–109. (in Chinese)

Qin W C, Xue X Y, Zhang S M, Gu W, Wang B K. Droplet deposition and efficiency of fungicides sprayed with small UAV against wheat powdery mildew. Int J Agric & Biol Eng, 2018; 11(2): 27–32.

Gao Y Y, Zhang Y T, Zhang N, Niu L, Zheng W W, Yuan H Z. Primary studies on spray droplets distribution and control effects of aerial spraying using unmanned aerial vehicle (UAV) against wheat midge. Crops, 2013; 2: 139–142. (in Chinese)

Tang Q, Zhang R R, Chen L P, Xu M, Yi T C, Zhang B. Droplets movement and deposition of an eight-rotor agricultural UAV in downwash flow field. Int J Agric & Biol Eng, 2017; 10(3): 47–56.

Mayeed M S, Darveau G. Designing an Unmanned Aerial Vehicle for specific aerial applications of insecticides and herbicides. In: ASME 2016 International Mechanical Engineering Congress and Exposition, Arizona, USA, 2016; 14: 8. doi: 10.1115/IMECE2016-65936.

Wang C L, He X K, Wang X N, Wang Z C, Wang S L, Li L L, et al. Testing method and distribution characteristics of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle. Int J Agric & Biol Eng, 2018; 11(2): 18–26.

Yang F B, Xue X Y, Cai C, Zhou Q Q. Effect of down wash airflow in hover on droplet motion law for multi-rotor unmanned plant protection machine. Trans CSAE, 2018a; 34(2): 64–73. (in Chinese)

Yang F B, Xue X Y, Cai C, Sun Z, Zhou Q Q. Numerical simulation and analysis on spray drift movement of multirotor plant protection unmanned aerial vehicle. Energies, 2018b; 11(9): 2399. doi: 10.3390/en11092399.

Yang Z L, Ge L Z, Qi L J, Cheng Y F, Wu Y L. Influence of UAV rotor down-wash airflow on spray width. Transactions of the CSAM, 2018; 49(1): 116–122. (in Chinese)

Zheng Y J, Yang S H, Liu X X, Wang J, Norton T, Chen, J, et al. The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV. Front. Agr. Sci. Eng., 2018; 5(2): 159–167.

Shi Q, Mao H, Guan X. Numerical simulation and experimental verification of the deposition concentration of an unmanned aerial vehicle. Appl Eng Agric, 2019; 35(3): 367–376.

Wen S, Han J, Ning Z H, Lan Y B, Yin X C, Zhang J T, et al. Numerical analysis and validation of spray distributions disturbed by quad-rotor drone wake at different flight speeds. Comput Electron Agric, 2019; 166: 10536.

Hunt E R, Daughtry C S. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture. Int J Remote Sens, 2018; 39(15-16): 5345–5376.

Maes W H, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci, 2018; 24(2): 152–164.

Aasen H, Honkavaara E, Lucieer A, Zarco-Tejada P. Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sens, 2018; 10(7): 1091. doi: 10.3390/rs10071091.

Yin N H, Liu R A, Zeng B B, Liu N. A review: UAV-based remote sensing. IOP Conference Series: Materials Science and Engineering, Shanghai, China, 2019; 490: 062014. doi: 10.1088/1757-899X/490/6/062014.

Lan Y B, Deng X L, Zeng G L. Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing. Smart Agric, 2019; 1(2): 1–19.

Rango A, Laliberte A, Herrick J E, Winters C, Havstad K, Steele, C, et al. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. J Appl Remote Sens, 2009; 3(1): 033542. doi: 10.1117/1.3216822.

Wang P, Luo X W, Zhou Z Y, Zang Y, Hu L. Key technology for remote sensing information acquisition based on micro UAV. Transactions of the CSAE, 2014; 30(18): 1–12. (in Chinese)

Psirofonia P, Samaritakis V, Eliopoulos P, Potamitis I. Use of unmanned aerial vehicles for agricultural applications with emphasis on crop protection: three novel case-studies. Int J Agric Sci Technol, 2017; 5: 30–39.

Hunt E R, Rondon S I. Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. J Appl Remote Sens, 2017; 11(2): 026013. doi: 10.1117/1.JRS.11.026013.

Albetis J, Duthoit S, Guttler F, Jacquin A, Goulard M, Poilvé H, et al. Detection of Flavescence dorée grapevine disease using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing, 2017; 9(4): 308. doi: 10.3390/rs9040308.

Ha J G, Moon H, Kwak J T, Hassan S I, Dang M, Lee O N, et al. Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. J Appl Remote Sens, 2017; 11(4): 042621. doi: 10.1117/1.JRS.11.042621.

Tetila E C, Machado B B, Belete N A, Guimarães D A, Pistori H. Identification of soybean foliar diseases using unmanned aerial vehicle images. IEEE Geoscience and Remote Sensing Letters, 2017; 14(12): 2190–2194.

Alexandridis T, Tamouridou A A, Pantazi X E, Lagopodi A, Kashefi J, Ovakoglou G, et al. Novelty detection classifiers in weed mapping: Silybum marianum detection on UAV multispectral images. Sens, 2017; 17(9): 2007. doi: 10.3390/s17092007.

Morley C G, Broadley J, Hartley R, Herries D, MacMorran D, McLean I G. The potential of using unmanned aerial vehicles (UAVs) for precision pest control of possums (Trichosurus vulpecula). Rethinking Ecology, 2017; 2: 27–39.

Castaldi F, Pelosi F, Pascucci S, Casa R. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precis Agric, 2017; 18(1): 76–94.

Tamouridou A, Alexandridis T, Pantazi X, Lagopodi A, Kashefi J, Kasampalis D, et al. Application of multilayer perceptron with automatic relevance determination on weed mapping using UAV multispectral imagery. Sensors, 2017; 17(10): 2307. doi: 10.3390/s17102307.

Pantazi X E, Tamouridou A A, Alexandridis T K, Lagopodi A L, Kashefi J, Moshou D. Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery. Comput Electron Agric, 2017; 139: 224–230.

Sa I, Popović M, Khanna R, Chen Z, Lottes P, Liebisch F, et al. WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sensing, 2018; 10(9): 1423. doi: 10.3390/rs10091423.

De Castro A I, Torres-Sánchez J, Peña J M, Jiménez-Brenes F M, Csillik O, López-Granados F. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sensing, 2018; 10(2): 285. doi: 10.3390/rs10020285.

Huang H S, Deng J Z, Lan Y B, Yang A Q, Deng X L, Wen S, et al. Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors, 2018a; 18(10): 3299. doi: 10.3390/s18103299.

Huang H S, Deng J J, Lan Y B, Yang A Q, Deng X L, Zhang L. A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PloS One, 2018; 13(4): e0196302. doi: 10.1371/journal.pone.01963023.

Huang H S, Lan Y B, Deng J Z, Yang A Q, Deng X L, Zhang L, et al. A semantic labeling approach for accurate weed mapping of high resolution UAV imagery. Sensors, 2018; 18(7): 2113. doi: 10.3390/s18072113.

Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C. UAV-based crop and weed classification for smart farming. 2017 IEEE International Conference on Robotics and Automation, Singapore, 2017; pp.3024–3031.

Wang S B, Liu H T, Han Y, Chen J, Pan Y, Cao Y, et al. Low-altitude remote sensing based on convolutional neural network for weed classification in ecological irrigation area. IFAC Papers OnLine, 2018; 51(17): 298–303.

Wang Z, Chu G K, Zhang H J, Liu S X, Huang X C, Gao F R, et al. Identification of diseased empty rice panicles based on Haar-like feature of UAV optical image. Trans CSAE, 2018; 34(20): 73–82. (in Chinese)

Huang H S, Deng J Z, Lan Y B, Yang A, Deng X L, Zhang L, et al. A two-stage classification approach for the detection of spider mite-infested cotton using UAV multispectral imagery. Remote Sensing Letters, 2018d; 9(10): 933–941.

Vanegas F, Bratanov D, Powell K, Weiss J, Gonzalez F. A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors, 2018; 18(1): 260. doi: 10.3390/s18010260.

Zhao X Y, Zhang J, Zhang D Y, Zhou X G, Liu X H, Xie J. Comparison between the effects of visible light and multispectral sensor based on low-altitude remote sensing platform in the evaluation of rice sheath blight. Spectrosc Spect Anal, 2019; 39(4): 1192–1198. (in Chinese)

Lan Y B, Zhu Z H, Deng X L, Lian B Z, Huang J Y, Huang Z X, et al. Monitoring and classification of citrus Huanglongbing based on UAV hyperspectral remote sensing. Transactions of the CSAE, 2019; 35(3): 92–100. (in Chinese)

Su J Y, Liu C J, Hu X P, Xu X M, Guo L, Chen W H. Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Comput Electron Agric, 2019; 167: 10503. doi: 10.1016/ j.compag.2019.105035.

Lan Y B, Thomson S J, Huang Y B, Hoffmann W C, Zhang H H. Current status and future directions of precision aerial application for site-specific crop management in the USA. Comput Electron Agric, 2010; 74(1): 34–38.

Wang L, Lan Y B, Hoffmann W C, Fritz B K, Chen D, Wang S M. Design of variable spraying system and influencing factors on droplets deposition of small UAV. Transactions of the CSAM, 2016; 47(1): 15–22. (in Chinese)

Wang L H, Gan H M, Yue X J, Lan Y B, Wang J, Liu Y X, et al. Design of a precision spraying control system with unmanned aerial vehicle based on image recognition. Journal of South China Agricultural University, 2016; 37(6): 23–30. (in Chinese)

Wang D S, Zhang J X, Li W, Xiong B, Zhang S L, Zhang W Q. Design and test of dynamic variable spraying system of plant protection UAV. Transactions of the CSAM, 2017; 48(5): 86–93. (in Chinese)

Wen S, Zhang Q Y, Deng J Z, Lan Y B, Yin X C, Shan J. Design and experiment of a variable spray system for unmanned aerial vehicles based on PID and PWM Control. Appl Sci, 2018; 8(12): 2482. doi: 10.3390/aapp8122482.

Lian Q, Tan F, Fu X M, Zhang P, Liu X, Zhang W. Design of precision variable-rate spray system for unmanned aerial vehicle using automatic control method. Int J Agric & Biol Eng, 2019; 12(2): 29–35.

Wen S, Zhang Q Y, Yin X C, Lan Y B, Zhang J T, Ge Y F. Design of plant protection UAV variable spray system based on neural networks. Sens, 2019; 19(5): 1112. doi: 10.3390/s19051112.

Campos J, Llop J, Gallart M, García-Ruiz F, Gras A, Salcedo R, et al. Development of canopy vigour maps using UAV for site-specific management during vineyard spraying process. Precis Agric, 2019; 20(6): 1136–1156.

Hunter J E, Gannon T W, Richardson R J, Yelverton F, Leon R G. Integration of remote-weed mapping and an autonomous spraying unmanned aerial vehicle for site-specific weed management. Pest Manag Sci, 2020; 76(4): 1386–1392.

Zhang S, Xue X S, Qin W, Sun Z, Ding S, Zhou L. Simulation and experimental verification of aerial spraying drift on N-3 unmanned spraying helicopter. Trans CSAE, 2015; 31(3): 87–93. (in Chinese)

Zhou L P, He Y. Simulation and optimization of multi spray factors in UAV. 2016 ASABE Annual International Meeting, Florida, USA, 2016.

Zhang H C, Zheng J Q, Zhou H P, Dorr, G J. Droplet deposition distribution and off-target drift during pesticide spraying operation. Transactions of the CSAM, 2017; 48(8): 114–122. (in Chinese)

Zheng Y J, Yang S H, Zhao C J, Chen L P, Lan Y B, Tan Y. Modelling operation parameters of UAV on spray effects at different growth stages of corns. Int J Agric & Biol Eng, 2017; 10(3): 57–66.

Wang X N, He X K, Wang C L, Wang Z C, Li L L, Wang S L, et al. Spray drift characteristics of fuel powered single-rotor UAV for plant protection. Transactions of the CSAE, 2017; 33(1): 117–123. (in Chinese)

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 UASs. Int J Agric & Biol Eng, 2019; 12(2): 10–17.

Kang T G, Lee C S, Choi D K, Jun H J, Koo Y M, Kang T H. Development of aerial application system attachable to unmanned helicopter-basic spraying characteristics for aerial application system. J Biosyst Eng, 2010; 35(4): 215–223.

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, 2012; 43(12): 94–96. (in Chinese)

Qin W C, Xue X Y, Zhang S C, Gu W, Chen C. Optimization and test of spraying parameters for P20 multi-rotor electric unmanned aerial vehicle based on response surface method. Journal of Jiangsu University, 2016; 37(5): 548–555. (in Chinese)

Woldt W, Martin D, Lahteef M, Kruger G, Wright R, McMechan J, et al. Field evaluation of commercially available small unmanned aircraft crop spray systems. 2018 ASABE Annual International Meeting, Michigan, USA, 2018; pp.1–15.

Hunter J E, Gannon T W, Richardson R J, Yelverton F, Leon R G. Coverage and drift potential associated with nozzle and speed selection for herbicide applications using an unmanned aerial sprayer. Weed Technol, 2020; 34(2): 235–240.

Giles D, Billing R. Deployment and performance of a UAV for crop spraying. Chem Eng Trans, 2015; 44: 307–312.

Zhang P, Deng L, Lyu Q, He S L, Yi S L, Liu Y D, et al. Effects of citrus tree-shape and spraying height of small unmanned aerial vehicle on droplet distribution. Int J Agric & Biol Eng, 2016; 9(4): 45–52.

Chen S D, Lan Y B, Zhou Z Y, Liao J, Zhu Q Y. Effects of spraying parameters of small protection UAV on droplets deposition distribution in citrus canopy. Journal of South China Agricultural University, 2017; 38(5): 97–102. (in Chinese)

Teske M E, Bowers J F, Rafferty J E, Barry J W. FSCBG: An aerial spray dispersion model for predicting the fate of released material behind aircraft. Environ Toxicol Chem, 1993; 12(3): 453–464.

Teske M E, Bird S L, Esterly D M, Curbishley T B, Ray S L, Perry S G. AgDrift®: A model for estimating near-field spray drift from aerial applications. Environ Toxicol Chem, 2002; 21(3): 659–671.

Teske M E, Thistle H W, Schou W C, Miller P C H, Strager J M, Richardson B, et al. A review of computer models for pesticide deposition prediction. Transactions of the ASABE, 2011; 54(3): 789–801.

Ru Y, Zhu C Y, Bao R. Spray drift model of droplets and analysis of influencing factors based on wind tunnel. Transactions of the CSAM, 2014; 45(10): 66–72. (in Chinese)

Azizpanah A, Rajabipour A, Alimardani R, Kheiralipour K, Mohammadi V. Precision spray modeling using image processing and artificial neural network. Agricultural Engineering International: CIGR Journal, 2015; 17(2): 65–74.

Zhang H C, Gary D, Zheng J Q, Zhou H P, Yu J. Wind tunnel experiment and regression model for spray drift. Trans CSAE, 2015; 31(3): 94–100. (in Chinese)

Wang L, Chen D, Yao Z, Ni X D, Wang S M. Research on the prediction model and its influencing factors of droplet deposition area in the wind tunnel environment based on UAV spraying. IFAC Papers OnLine, 2018a; 51(17): 274–279.

Wang J, Lan Y B, Zhang H H, Zhang Y L, Wen S, Yao W X, et al. Drift and deposition of pesticide applied by UAV on pineapple plants under different meteorological conditions. Int J Agric Biol Eng, 2018; 11(6): 5–12.

Fesal S N M, Fawzi M, Omar Z. A numerical analysis of flat fan aerial crop spray. 2017 IOP Conference Series: Materials Science and Engineering, Songhkla, Thailand, 2017; 243(1): 012044. doi: 10.1088/1757-899X/243/1/012044.

Omar Z, Qiang K Y, Mohd S, Rosly N. CFD simulation of aerial crop spraying. 2016 IOP Conference Series: Materials Science and Engineering, Melaka, Malaysia, 2016; 160(1): 012028. doi: 10.1088/1757-899X/160/1/012028.

Wang L, Chen D, Zhang M C, Wang Y, Yao Z, Wang S M. CFD simulation of low-attitude droplets deposition characteristics for UAV based on multi-feature fusion. IFAC Papers OnLine, 2018b; 51(17): 648–653.

Parra H G, Morales V D A, Garcia E E G. Multiphase CFD simulation of photogrammetry 3D model for UAV crop spraying. In: New Knowledge in Information Systems and Technologies. Switzerland: Springer, 2019; 930: 812–822.

Teske M E, Wachspress D A, Thistle H W. Prediction of aerial spray release from UAVs. Trans ASABE, 2018; 61(3): 909–918.

Wachspress D A, Yu K, Saberi H A, Hasbun M J, Ho J C, Yeo H. Helicopter rotor airload predictions with a comprehensive rotorcraft analysis. Proc. 68th Annual Forum of the American Helicopter Society, Texas, USA, 2012.

Keller J D, Wachspress D A. Validation of the CHARM aerodynamic module for engineering and piloted simulation of next-generation tilt rotor aircraft. Ewing, NJ: Continuum Dynamics, Inc. 2012; Report No. 1203.

Wachspress D A, Whitehouse G R, Keller J D, Yu K, Gilmore P, Dorsett M, et al. A high-fidelity brownout model for flight simulations and trainers. Proc. 65th Annual Forum of the American Helicopter Society, Texas, USA, 2009; 1: 278–301.

Wachspress D A, Keller J D, Quackenbush T R, Whitehouse G R, Yu K. High-fidelity rotor aerodynamic module for real-time rotorcraft flight simulation. Proc. 64th Annual forum of the American helicopter society, Montreal, Canada, 2008; 64(1): 288.

Vasudevan A, Kumar D A, Bhuvaneswari N S. Bhuvaneswari, Precision farming using unmanned aerial and ground vehicles. 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, 2016; 146–150.

VineyardROBOT project, Available: https://cordis.europa.eu/ project/rcn/111031/factsheet/it. Accessed on [2020-01-07].




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