IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i2p131-d727871.html
   My bibliography  Save this article

Analysis of the Influence of Parameters of a Spraying System Designed for UAV Application on the Spraying Quality Based on Box–Behnken Response Surface Method

Author

Listed:
  • Dashuai Wang

    (Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China
    College of Engineering, China Agricultural University, Beijing 100083, China)

  • Sheng Xu

    (Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China)

  • Zhuolin Li

    (Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China
    School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Wujing Cao

    (Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China)

Abstract

With the development of precision agriculture (PA), low-altitude and low-volume spraying based on unmanned aerial vehicles (UAVs) is playing an increasingly important role in the control of crop diseases, pests, and weeds. However, the aerial spraying quality and droplet drift are affected by many factors, some of which are controllable (e.g., flight and spraying parameters) and some of which are not (e.g., environmental parameters). In order to study the influence of spraying parameters on the UAV-based spraying performance, we propose a UAV-compatible spraying system and a customized experimental platform in this work. Through single-factor test and Box–Behnken response surface methods, four influencing factors, namely spraying height, flow rate, distance between nozzles, and pulse width modulation (PWM) duty cycle, were studied under indoor conditions. Variance analysis and multiple quadratic regression fitting were performed on the test data by using Design-Expert 8.0.5B software, and quadratic polynomial regression models of effective spraying width, droplet coverage density, coefficient of variation, and droplet coverage rate were established. Based on the Z-score standardization, a mathematical model of the comprehensive score with four factors was established to evaluate the spraying quality and predict optimal spraying parameters. Test results indicate that the effect intensity of four influencing factors from strong to weak is PWM duty cycle, flow rate, distance between nozzles, and spraying height, and their optimal values are 98.65%, 1.74 L/min, 1.0 m, and 1.60 m, respectively. Additionally, verification experimental results demonstrate that the deviation between the predicted comprehensive score and the actual value was less than 6%. This paper can provide a reference for the design and optimization of UAV spraying systems.

Suggested Citation

  • Dashuai Wang & Sheng Xu & Zhuolin Li & Wujing Cao, 2022. "Analysis of the Influence of Parameters of a Spraying System Designed for UAV Application on the Spraying Quality Based on Box–Behnken Response Surface Method," Agriculture, MDPI, vol. 12(2), pages 1-14, January.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:131-:d:727871
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/2/131/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/2/131/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anthony King, 2017. "Technology: The Future of Agriculture," Nature, Nature, vol. 544(7651), pages 21-23, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou Yang & Jiaxiang Yu & Jieli Duan & Xing Xu & Guangsheng Huang, 2023. "Optimization-Design and Atomization-Performance Study of Aerial Dual-Atomization Centrifugal Atomizer," Agriculture, MDPI, vol. 13(2), pages 1-19, February.
    2. Gonçalo C. Rodrigues, 2022. "Precision Agriculture: Strategies and Technology Adoption," Agriculture, MDPI, vol. 12(9), pages 1-4, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alison Kennedy & Jessie Adams & Jeremy Dwyer & Muhammad Aziz Rahman & Susan Brumby, 2020. "Suicide in Rural Australia: Are Farming-Related Suicides Different?," IJERPH, MDPI, vol. 17(6), pages 1-13, March.
    2. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    3. Yaoyao Wang & Yuanpei Kuang, 2023. "Evaluation, Regional Disparities and Driving Mechanisms of High-Quality Agricultural Development in China," Sustainability, MDPI, vol. 15(7), pages 1-20, April.
    4. Dimitrios Loukatos & Vasileios Arapostathis & Christos-Spyridon Karavas & Konstantinos G. Arvanitis & George Papadakis, 2024. "Power Consumption Analysis of a Prototype Lightweight Autonomous Electric Cargo Robot in Agricultural Field Operation Scenarios," Energies, MDPI, vol. 17(5), pages 1-24, March.
    5. Thorsøe, Martin Hvarregaard & Noe, Egon Bjørnshave & Lamandé, Mathieu & Frelih-Larsen, Ana & Kjeldsen, Chris & Zandersen, Marianne & Schjønning, Per, 2019. "Sustainable soil management - Farmers’ perspectives on subsoil compaction and the opportunities and barriers for intervention," Land Use Policy, Elsevier, vol. 86(C), pages 427-437.
    6. Rübcke von Veltheim, Friedrich & Claussen, Frans & Heise, Heinke, 2020. "Autonomous Field Robots in Agriculture: A Qualitative Analysis of User Acceptance According to Different Agricultural Machinery Companies," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305587, German Association of Agricultural Economists (GEWISOLA).
    7. Ilya Kuzminov & Pavel Bakhtin & Elena Khabirova & Maxim Kotsemir & Alina Lavrynenko, 2018. "Mapping the Radical Innovations in Food Industry: A Text Mining Study," HSE Working papers WP BRP 80/STI/2018, National Research University Higher School of Economics.
    8. Eirini Aivazidou & Naoum Tsolakis, 2023. "Transitioning towards human–robot synergy in agriculture: A systems thinking perspective," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(3), pages 536-551, May.
    9. Milyausha Lukyanova & Vitaliy Kovshov & Zariya Zalilova & Vasily Lukyanov & Irek Araslanbaev, 2021. "A systemic comparative economic approach efficiency of fodder production," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-17, December.
    10. Rübcke von Veltheim, Friedrich & Claussen, Frans & Heise, Heinke, 2020. "Autonomous Field Robots in Agriculture: A Qualitative Analysis of User Acceptance According to Different Agricultural Machinery Companies," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305587, German Association of Agricultural Economists (GEWISOLA).
    11. Friedrich Rübcke von Veltheim & Heinke Heise, 2020. "The AgTech Startup Perspective to Farmers Ex Ante Acceptance Process of Autonomous Field Robots," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    12. Nathan J. Shipley & William P. Stewart & Carena J. Riper, 2022. "Negotiating agricultural change in the Midwestern US: seeking compatibility between farmer narratives of efficiency and legacy," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 39(4), pages 1465-1476, December.
    13. Ting Zhang & Qingdong Zeng & Fan Ji & Honghong Wu & Rodrigo Ledesma-Amaro & Qingshan Wei & Hao Yang & Xuhan Xia & Yao Ren & Keqing Mu & Qiang He & Zhensheng Kang & Ruijie Deng, 2023. "Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    14. Muhammad Junaid & Asadullah Shaikh & Mahmood Ul Hassan & Abdullah Alghamdi & Khairan Rajab & Mana Saleh Al Reshan & Monagi Alkinani, 2021. "Smart Agriculture Cloud Using AI Based Techniques," Energies, MDPI, vol. 14(16), pages 1-15, August.
    15. Michels, Marius & von Hobe, Cord-Friedrich & Mußhoff, Oliver, 2020. "Understanding the Adoption of Drones in German Agriculture," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305579, German Association of Agricultural Economists (GEWISOLA).
    16. Kitonsa, H. & Kruglikov, S. V., 2018. "Significance of drone technology for achievement of the United Nations sustainable development goals," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 4(3), pages 115-120.
    17. Anja Gaudig & Bernd Ebersberger & Andreas Kuckertz, 2021. "Sustainability-Oriented Macro Trends and Innovation Types—Exploring Different Organization Types Tackling the Global Sustainability Megatrend," Sustainability, MDPI, vol. 13(21), pages 1-19, October.
    18. Michels, Marius & von Hobe, Cord-Friedrich & Mußhoff, Oliver, 2020. "Understanding the Adoption of Drones in German Agriculture," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305579, German Association of Agricultural Economists (GEWISOLA).
    19. Ehlers, Melf-Hinrich & Finger, Robert & El Benni, Nadja & Gocht, Alexander & Sørensen, Claus Aage Grøn & Gusset, Markus & Pfeifer, Catherine & Poppe, Krijn & Regan, Áine & Rose, David Christian & Wolf, 2022. "Scenarios for European agricultural policymaking in the era of digitalisation," Agricultural Systems, Elsevier, vol. 196(C).
    20. Friedrich Rübcke von Veltheim & Heinke Heise, 2021. "German Farmers’ Attitudes on Adopting Autonomous Field Robots: An Empirical Survey," Agriculture, MDPI, vol. 11(3), pages 1-19, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:131-:d:727871. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.