Search

Menu
Search
in Ecology

Plant spraying quality when used by drone-robots

127 Views


Abstract

The study aimed to evaluate how airflow from a small drone’s rotor affects the accuracy and consistency of liquid coverage on individual plants or small plant groups during low-altitude spraying. The research focused on the future possibility of using drones – unmanned aerial vehicles (UAVs) as autonomous robots for plant protection. The effects of UAV propellers’ rotation on changes in droplet stream, produced from a single spray flat fan nozzle, as well as the influence of altitude, flight speed, and leaf area index (LAI), on the liquid coverage uniformity (CU) of application on plants were examined. The rotational speeds of the UAV propellers were set at 0.0, 5000, and 6400 rpm. Rapeseed and potato plants were selected for testing because they are common food crops and sources of raw materials for biofuel production. The UAV, on a laboratory test stand, was moved at two heights (H = 0.5 and 1.0 m) at two speeds (v = 0.54 and 1.0 m·s-1). The airflow from the UAV’s rotors narrowed the droplet stream produced by the nozzle by about 20% and increased the liquid volume in the center of the droplet stream, especially at a UAV height of H = 1.0 m. The leaf area index (LAI) for rapeseed was 0.877, and for potatoes it was 6.273. These LAI differences resulted in the liquid sprayed from the UAV settling more evenly on rapeseed plants than on potato plants. UAV flight altitude and leaf area index (LAI) appeared to be key factors affecting the amount of liquid applied to plants. Lower altitudes (H = 0.5 m) improved liquid application uniformity and enabled deeper penetration into dense foliage. High LAI values significantly hampered liquid penetration into lower plant levels and reduced the uniformity of liquid application.

Data availability

https://doi.org/10.5281/zenodo.17221483

References

  1. Jang, G. et al. Review: Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application. Remote Sens. 12(6), 998. https://doi.org/10.3390/rs12060998 (2020).

    Google Scholar 

  2. Panday, U. S. et al. Correlating the plant height of wheat with above-ground biomass and crop yield using drone imagery and crop surface model: A case study from Nepal. Drones 4(3), 28. https://doi.org/10.3390/drones4030028 (2020).

    Google Scholar 

  3. Raj, R., Walker, J. P., Pingale, R., Banoth, B. N. & Jagarlapudi, A. Leaf nitrogen content Estimation using top-of-canopy airborne hyperspectral data. I Int. J. Appl. Earth Obs Geoinf. 104, 102584. https://doi.org/10.1016/j.jag.2021.102584 (2021).

    Google Scholar 

  4. Pachuta, A. et al. Propellers spin rate effect of a spraying drone on quality of liquid deposition in a crown of young Spruce. Agriculture 13 (8), 1584. https://doi.org/10.3390/agriculture13081584 (2023).

    Google Scholar 

  5. Gatkal, N. R. et al. Effect of operational parameters on spray performance and drift characteristics of a UAV-based agrochemical application in pigeon pea crop to control thrips. Front. Plant. Sci. 16, 1570272. https://doi.org/10.3389/fpls.2025.1570272 (2025).

    Google Scholar 

  6. Karademir, S., Teter, Ş., Deniz, Y., Yildiz, F. & Yarici, İ. Design and implementation of a novel seeding drone. Sigma J. Eng. Nat. Sci. 43 (1), 189–198. https://doi.org/10.14744/sigma.2025.00014 (2025).

    Google Scholar 

  7. Han, J. et al. Impact of variable device structural changes on particle deposition distribution in multi-rotor UAV. Drones 8(10), 583. https://doi.org/10.3390/drones8100583 (2024).

    Google Scholar 

  8. Cai, G., Chen, B. M. & Lee, T. H. An overview on development of miniature unmanned rotorcraft systems. Front. Electr. Electron. Eng. China. 5, 1–14. https://doi.org/10.1007/s11460-009-0065-3 (2010).

    Google Scholar 

  9. Berner, B. et al. Spraying wheat plants with a drone moved at low altitudes. Agronomy 14 (9), 1894. https://doi.org/10.3390/agronomy14091894 (2024).

    Google Scholar 

  10. Zheng, Y. J. et al. Modeling operation parameters of UAV on spray effects at different growth stages of corns. Int. J. Agric. Biol. Eng. 10 (3), 57–66 (2017). https://ijabe.org/index.php/ijabe/article/view/2578

    Google Scholar 

  11. Song, X. P. et al. Intrusion of fall armyworm (Spodoptera frugiperda) in sugarcane and its control by drone in China. Sugar Tech. 22, 734–737. https://doi.org/10.1007/s12355-020-00799-x (2020).

    Google Scholar 

  12. Zhang, P. et al. Effects of spray parameters on the effective spray width of Single-Rotor drone in sugarcane plant protection. Sugar Tech. 23, 308–315. https://doi.org/10.1007/s12355-020-00890-3 (2021).

    Google Scholar 

  13. Shanmugam, O. S. et al. Comparative analysis of unmanned aerial vehicle and conventional spray systems for the maize fall armyworm spodoptera Frugiperda (J.E. Smith) (Lepidoptera; Noctuidae) management. Plant. Prot. Sci. 60(2), 181–192. https://doi.org/10.17221/96/2023-PPS (2024).

    Google Scholar 

  14. Safaeinezhad, M., Ghasemi-Nejad-Raeini, M. & Taki, M. Performance evaluation of spraying drones compared to boom sprayers for spray applications. Sci. Rep. 15, 41521. https://doi.org/10.1038/s41598-025-25353-1 (2025).

    Google Scholar 

  15. Wang, S. L. et al. Performances evaluation of four typical unmanned aerial vehicles used for pesticide application in China. Int. J. Agric. Biol. Eng. 10 (4), 22–31 (2017). https://ijabe.org/index.php/ijabe/article/view/3219/0

    Google Scholar 

  16. 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. 11 (2), 27–32 (2018). https://ijabe.org/index.php/ijabe/article/view/3157

    Google Scholar 

  17. Gao, P., Zhang, Y., Zhang, L., Noguchi, R. & Ahamed, T. Development of a recognition system for spraying areas from unmanned aerial vehicles using a machine learning approach. Sensors 19 (2), 313. https://doi.org/10.3390/s19020313 (2019).

    Google Scholar 

  18. Wang, G., Jiang, C., Tao, G. & Ye, C. Dynamic path planning based on the fusion of improved RRT and DWA algorithms. 4th International Conference on Mechatronics Technology and Intelligent Manufacturing ICMTIM, pp. 534–538. (2023). https://doi.org/10.1109/icmtim58873.2023.10246738

  19. DJI AGRAS T100. https://ag.dji.com/t100?site=ag&from=nav

  20. Wang, L. et al. Design of variable spraying system and influencing factors on droplets deposition of small UAV. Trans. Chin. Soc. Agric. Mach. 47 (1), 15–22. https://doi.org/10.6041/j.issn.1000-1298.2016.01.003 (2016).

    Google Scholar 

  21. Berner, B., Pachuta, A. & Chojnacki, J. Estimation of liquid deposition on corn plants sprayed from a drone. In The 25th International PhD Students Conference MENDELNET, 403–407 (2018). https://mendelnet.cz/pdfs/mnt/2018/01/85.pdf

  22. Wang, C. L. et al. Testing method and distribution characteristics of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle. Int. J. Agric. Biol. Eng. 11(2), 18–26. https://doi.org/10.25165/j.ijabe.20181102.3187 (2018).

    Google Scholar 

  23. Wang, C. et al. Assessment of spray deposition, drift and mass balance from unmanned aerial vehicle sprayer using an artificial vineyard. Sci. Total Environ. 777, 146181. https://doi.org/10.1016/j.scitotenv.2021.146181 (2021).

    Google Scholar 

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

    Google Scholar 

  25. Zhang, H. et al. Numerical simulation of airflow field from a six–rotor plant protection drone using lattice Boltzmann method. Biosyst. Eng. 197, 336–351. https://doi.org/10.1016/j.biosystemseng.2020.07.018 (2020).

    Google Scholar 

  26. Zhang, H. et al. WSPM-system: Providing real data of rotor speed and pitch angle for numerical simulation of downwash airflow from a multirotor UAV sprayer. Agriculture 11(11), 1038. https://doi.org/10.3390/agriculture11111038 (2021a).

    Google Scholar 

  27. Wang, J. et al. Numerical simulation and analysis of droplet drift motion under different wind speed environments of single-rotor plant protection UAVs. Drones 7(2), 128. https://doi.org/10.3390/drones7020128 (2023a).

    Google Scholar 

  28. Chen, Q. et al. CFD analysis and RBFNN-based optimization of spraying system for a six-rotor unmanned aerial vehicle (UAV) sprayer. Crop Prot. 174, 106433. https://doi.org/10.1016/j.cropro.2023.106433 (2023).

    Google Scholar 

  29. Dong, S. J. et al. CFD-Based pesticide selection for a nozzle used in a Six-Rotor UAV in hover mode for tea spraying. Pest Manag Sci. 79 (5), 1963–1976. https://doi.org/10.1002/ps.7371 (2023).

    Google Scholar 

  30. Liu, Z. et al. Study on the characteristics of downwash field range and consistency of spray deposition of agricultural UAVs. Agriculture 14 (6), 931. https://doi.org/10.3390/agriculture14060931 (2024).

    Google Scholar 

  31. Karpiński, P., Czyż, Z. & Parafiniuk, S. Numerical modelling of the multiphase flow in an agricultural Hollow cone nozzle. Appl. Sci. 15 (13), 7214. https://doi.org/10.3390/app15137214 (2025).

    Google Scholar 

  32. Qin, W. C. et al. Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Crop Prot. 85, 79–88. https://doi.org/10.1016/j.cropro.2016.03.01 (2016).

    Google Scholar 

  33. Desa, H., Azizan, M. A., Ishak, N. & Hang, T. X. Experimental analysis of flight altitude for enhanced agricultural drone spraying performance. J. Robot Netw. Artif. Life. 10 (1), 71–76 (2023). https://www.jstage.jst.go.jp/article/jrnal/10/1/10_11/_pdf/-char/en

    Google Scholar 

  34. Herbst, A., Glaser, M. & Bartsch, K. U. Spray drift from application of plant protection products with drones in vineyards. J. Kult Pflanz. 75, 151–157. https://doi.org/10.5073/JfK.2023.05-06.04 (2023).

    Google Scholar 

  35. Tang, Q. et al. Droplets movement and deposition of an eight-rotor agricultural UAV in downwash flow field. Int. J. Agric. Biol. Eng. 10 (3), 47–56 (2017). https://www.ijabe.org/index.php/ijabe/article/view/3075

    Google Scholar 

  36. Zhang, Y. et al. Near ground platform development to simulate uav aerial spraying and its spraying test under different conditions. Comput. Electron. Agric. 148, 8–18. https://doi.org/10.1016/j.compag.2017.08.004 (2018).

    Google Scholar 

  37. Wang, C. et al. Spray drift characteristics test of unmanned aerial vehicle spray unit under wind tunnel conditions. Int. J. Agric. Biol. Eng. 13, 13–21. https://doi.org/10.25165/j.ijabe.20201303.5716 (2020).

    Google Scholar 

  38. Liu, Q., Chen, S., Wang, G. & Lan, Y. Drift evaluation of a quadrotor unmanned aerial vehicle (UAV) sprayer: Effect of liquid pressure and wind speed on drift potential based on wind tunnel test. Appl. Sci. 11(16), 7258. https://doi.org/10.3390/app11167258 (2021).

    Google Scholar 

  39. Sinha, J. P. Aerial robot for smart farming and enhancing farmers’ net benefit. Indian J. Agric. Sci. 90(2), 258–267. https://doi.org/10.56093/ijas.v90i2.98997 (2020).

    Google Scholar 

  40. Azfar, S. et al. IoT-based cotton plant pest detection and smart-response system. Appl. Sci. 13(3), 1851. https://doi.org/10.3390/app13031851 (2023).

    Google Scholar 

  41. Sharma, S., Verma, K. & Hardaha, P. Implementation of artificial intelligence in agriculture. J. Comput. Cogn. Eng. 2 (2), 155–162. https://doi.org/10.47852/bonviewJCCE2202174 (2023).

    Google Scholar 

  42. Akdoğan, C., Özer, T. & Oğuz, Y. Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques. Robot. Intell. Autom. 44(1), 131–151. https://doi.org/10.1108/RIA-05-2023-0068 (2024).

    Google Scholar 

  43. Ehrampoosh, A. et al. Intelligent weed management using aerial image processing and precision herbicide spraying: An overview. Crop Prot. 194, 107206. https://doi.org/10.1016/j.cropro.2025.107206 (2025).

    Google Scholar 

  44. Maes, W. H. & Steppe, K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 24, 152–164. https://doi.org/10.1016/j.tplants.2018.11.007 (2019).

    Google Scholar 

  45. García-Munguía, A. et al. A review of drone technology and operation processes in agricultural crop spraying. Drones 8(11), 674. https://doi.org/10.3390/drones8110674 (2024).

    Google Scholar 

  46. Bumbaca, S. & Borgogno-Mondino, E. Supporting screening of new plant protection products through a multispectral photogrammetric approach integrated with AI. Agronomy 14(2), 306. https://doi.org/10.3390/agronomy14020306 (2024).

    Google Scholar 

  47. Nørremark, M., Griepentrog, H. W., Nielsen, J. & Søgaard, H. T. The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosyst. Eng. 101(4), 396–410. https://doi.org/10.1016/j.biosystemseng.2008.09.007 (2008).

    Google Scholar 

  48. Yan, B. X., Wu, G. W., Xiao, Y. J., Mei, H. & Meng, Z. Development and evaluation of a seed position mapping system. Comput. Electron. Agric. 190, 106446. https://doi.org/10.1016/j.compag.2021.106446 (2021).

    Google Scholar 

  49. Akanksha, M. et al. Robotics redefining wheat farming: Bridging efficiency and sustainability. Plant Sci. Today 12(sp3), 1–9. https://doi.org/10.14719/pst.8322 (2025).

    Google Scholar 

  50. Wu, J. et al. Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography. Plant Methods 18, 129. https://doi.org/10.1186/s13007-022-00966-z (2022).

    Google Scholar 

  51. Zhu, H., Dorner, J., Rowland, D., Derksen, R. & Ozkan, H. Spray penetration into peanut canopies with hydraulic nozzle tips. Biosyst. Eng. 87(3), 275–283. https://doi.org/10.1016/j.biosystemseng.2003.11.012 (2004).

    Google Scholar 

  52. Liao, J. et al. The relations of leaf area index with the spray quality and efficacy of cotton defoliant spraying using unmanned aerial systems (UASs). Comput. Electron. Agric. 169, 105228. https://doi.org/10.1016/j.compag.2020.105228 (2020).

    Google Scholar 

  53. Stuhne, D., Vasiljević, G., Bogdan, S. & Kovačić, Z. Design and validation of a wireless drone Docking station. In International Conference on Unmanned Aircraft Systems (ICUAS), 652–657. https://doi.org/10.1109/ICUAS57906.2023.10156589 (2023).

  54. Florczak, D., Jacewicz, M., Kopyt, A. & Głębocki, R. Autonomous solar-powered docking station for quadrotor drones–Design and testing. Proc. Inst. Mech. Eng. G J. Aerosp. Eng. 239(15), 2054–2076. https://doi.org/10.1177/09544100251331282 (2024).

    Google Scholar 

  55. Ahmed, S. et al. A data-driven dynamic obstacle avoidance method for liquid-carrying plant protection UAVs. Agronomy 12(4), 873. https://doi.org/10.3390/agronomy12040873 (2022).

    Google Scholar 

  56. Torres-Sanchez, J., Lopez-Granados, F., De Castro, A. I. & Pena-Barragan, J. M. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. Plos One. 8 (3), e58210. https://doi.org/10.1371/journal.pone.0058210 (2013).

    Google Scholar 

  57. Farid, A. M., Mouhoub, M., Online Collaborative, U. A. V. Online collaborative UAV path planning for mapping and spraying missions. Robot. Comput. Vis. Intell. Syst. ROBOVIS 2025 Commun. Comput. Inf. Sci. 2629, 193–207. https://doi.org/10.1007/978-3-032-00986-9_14 (2025).

    Google Scholar 

  58. Srivastava, K., Pandey, P. C. & Sharma, J. K. An approach for route optimization in applications of precision agriculture using UAVs. Drones 4(3), 58. https://doi.org/10.3390/drones4030058 (2020).

    Google Scholar 

  59. Zhang, G. et al. A shortest distance priority UAV path planning algorithm for precision agriculture. Sensors 24(23), 7514. https://doi.org/10.3390/s24237514 (2024).

    Google Scholar 

  60. Chen, C. J. et al. Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access. 9, 21986–21997. https://doi.org/10.1109/access.2021.3056082 (2021).

    Google Scholar 

  61. Waqas, A., Kang, D. & Cha, Y-J. Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Struct. Health Monit. 23 (2), 971–990. https://doi.org/10.1177/14759217231177314 (2023).

    Google Scholar 

  62. Li, J., Xiong, X., Yan, Y. & Yang, Y. A survey of indoor UAV obstacle avoidance research. IEEE Access 11, 51861–1891. https://doi.org/10.1109/access.2023.3262668 (2023).

    Google Scholar 

  63. Yang, Y.-S. & Juang, J.-G. Path planning and obstacle avoidance of formation flight. Sensors 25(8), 2447. https://doi.org/10.3390/s25082447 (2025).

    Google Scholar 

  64. Xu, Z. et al. A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles. IEEE Robot. Autom. Lett. 8, 4983–4990. https://doi.org/10.1109/lra.2023.3290415 (2023).

    Google Scholar 

  65. Panda, M., Das, B., Subudhi, B. & Pati, B. B. A comprehensive review of path planning algorithms for autonomous underwater vehicles. Int. J. Autom. Comput. 17, 321–352. https://doi.org/10.1007/s11633-019-1204-9 (2020).

    Google Scholar 

  66. Rodrigues, L. et al. <article-title update=“added”>Flight planning optimization of multiple UAVs for Internet of Things. Sensors 21(22), 7735. https://doi.org/10.3390/s21227735 (2021).

    Google Scholar 

  67. Lara-Molina, F. A. Optimization of coverage path planning for agricultural drones in Weed-Infested fields using semantic segmentation. Agriculture 15 (12), 1262. https://doi.org/10.3390/agriculture15121262 (2025).

    Google Scholar 

  68. Shen, K., Shivgan, R., Medina, J., Dong, Z. & Rojas-Cessa, R. Multidepot drone path planning with collision avoidance. IEEE Internet Things J. 9(17), 16297–16307. https://doi.org/10.1109/JIOT.2022.3151791 (2022).

    Google Scholar 

  69. Yang, Y., Xiong, X. & Yan, Y. UAV formation trajectory planning algorithms: A review. Drones 7(1), 62. https://doi.org/10.3390/drones7010062 (2023).

    Google Scholar 

  70. Tian, J. et al. Adversarial attacks and defenses for deep-learning-based unmanned aerial vehicles. IEEE Internet Things J. 9(22), 22399–22409. https://doi.org/10.1109/JIOT.2021.3111024 (2022).

    Google Scholar 

  71. Manohar, V., Kleski, C., Rasalkar, R. & Sengupta, R. RL based UAV trajectory optimization to avoid jammers and optimize wireless connectivity. In MILCOM 2024–2024 IEEE Military Communications Conference (MILCOM), 944–949. https://doi.org/10.1109/milcom61039.2024.10773865 (2024).

  72. Wang, B., Tian, J., Lyu, X. & Li, S. An efficient and low-delay SFC recovery method in the space–air–ground integrated aviation information network with integrated UAVs. Drones 9(6), 440. https://doi.org/10.3390/drones9060440 (2025).

    Google Scholar 

  73. Wang, P. et al. Development of an autonomous drone spraying control system based on the coefficient of variation of spray distribution. Comput. Electron. Agric. 227, 109529. https://doi.org/10.1016/j.compag.2024.109529 (2024).

    Google Scholar 

  74. Liao, J. 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. 12 (2), 10–17 (2019). https://www.ijabe.org/index.php/ijabe/article/view/4288

    Google Scholar 

  75. Hu, H. et al. Control effect on cotton aphids of insecticides sprayed with unmanned aerial vehicles under different flight heights and spray volumes. Int. J. Precis Agric. Aviat. 4 (1), 44–51 (2021). https://www.ijpaa.org/index.php/ijpaa/article/view/161

    Google Scholar 

  76. Chen, P. et al. Droplet deposition and control of planthoppers of different nozzles in Two-Stage rice with a quadrotor unmanned aerial vehicle. Agronomy 10 (2), 303. https://doi.org/10.3390/agronomy10020303 (2020).

    Google Scholar 

  77. Chen, P. et al. Characteristics of unmanned aerial spraying systems and related spray drift: A review. Front. Plant. Sci. 13, 870956. https://doi.org/10.3389/fpls.2022.870956 (2022).

    Google Scholar 

  78. Martin, D. E. et al. Spray deposition and drift as influenced by wind speed and spray nozzles from a remotely piloted aerial application system. Drones https://doi.org/10.3390/drones9010066 (2025).

    Google Scholar 

  79. Ribeiro, L. F. O. & Vitória, E. L. .d. Impact of application rate and spray nozzle on droplet distribution on watermelon crops using an unmanned aerial vehicle. Agriculture 14 (8), 1351. https://doi.org/10.3390/agriculture14081351 (2024).

    Google Scholar 

  80. Lopes, L. L., Cunha, J. P. A. R. & Nomelini, Q. S. S. Use of unmanned aerial vehicle for pesticide application in soybean crop. AgriEngineering 5 (4), 2049–2063. https://doi.org/10.3390/agriengineering5040126 (2023).

    Google Scholar 

  81. Ranabhat, S. & Price, R. Effects of flight heights and nozzle types on spray characteristics of unmanned aerial vehicle (UAV) sprayer in common field crops. AgriEngineering 7(2), 22. https://doi.org/10.3390/agriengineering7020022 (2025).

    Google Scholar 

  82. Yan, M. et al. Characteristics of the liquid sheet of Air-Induction spray. Agronomy 15 (6), 1270. https://doi.org/10.3390/agronomy15061270 (2025).

    Google Scholar 

  83. Yu, S. H., Kang, Y. & Lee, C. G. Comparison of the spray effects of air induction nozzles and flat fan nozzles installed on agricultural drones. Appl. Sci. 13 (20), 11552. https://doi.org/10.3390/app132011552 (2023).

    Google Scholar 

  84. Zhu, Z. et al. Optimization design and atomization performance of a multi-disc centrifugal nozzle for unmanned aerial vehicle sprayer. Agronomy 14(12), 2914. https://doi.org/10.3390/agronomy14122914 (2024).

    Google Scholar 

  85. Godyń, A. et al. Spray deposition, drift and equipment contamination for drone and conventional orchard spraying under European conditions. Agriculture 15(23), 2467. https://doi.org/10.3390/agriculture15232467 (2025).

    Google Scholar 

  86. Baio, F. H. R. et al. Characterization of the droplet population generated by centrifugal atomization nozzles of UAV sprayers. AgriEngineering 7(1), 15. https://doi.org/10.3390/agriengineering7010015 (2025).

    Google Scholar 

  87. Marques Filho, A. C. et al. Remotely piloted aircraft spraying in coffee cultivation: Effects of two spraying systems on drop deposition. Agriengineering 7(11), 379. https://doi.org/10.3390/agriengineering7110379 (2025).

    Google Scholar 

  88. Ahrens, K., Herbst, A. & Wegener, J. K. Field comparison of spray drift from unmanned aerial spraying systems using air-induction nozzles versus rotary atomizers. Front. Agron. 7, 1716113. https://doi.org/10.3389/fagro.2025.1716113 (2025).

    Google Scholar 

  89. Hu, H. et al. Design and performance test of a novel UAV air-assisted electrostatic centrifugal spraying system. Int. J. Agric. Biol. Eng. 15(5), 34–40. https://doi.org/10.25165/j.ijabe.20221505.6891 (2022).

    Google Scholar 

  90. Directive 2009/128/EC of the European Parliament and of the Council of 21 October 2009 Establishing A Framework for Community Action to Achieve the Sustainable Use of Pesticides (Text with EEA Relevance). https://eur-lex.europa.eu/eli/dir/2009/128/oj

Download references

Acknowledgements

This publication is supported within the project „Waste as an alternative source of energy“, reg. nr. CZ.02.01.01/00/23_021/0008590 under the Programme Johannes Amos Comenius.

Author information

Authors and Affiliations

  1. Pomeranian Agricultural Advisory Centre (PAAC – PODR), Lubań, Poland

    Bogusława Berner

  2. ENET Centre, CEET, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Jerzy Chojnacki, Jan Najser, Jan Kielar & Tomáš Najser

  3. Faculty of Mechanical Engineering and Energetics, Koszalin University of Technology, Koszalin, Poland

    Bogusława Berner, Jerzy Chojnacki & Leon Kukiełka

  4. Institute of Technology and Life Sciences – National Research Institute (ITP – PIB), Falenty, Poland

    Leszek Sergiel

Authors

  1. Bogusława Berner
    View author publications

    Search author on:PubMed Google Scholar

  2. Jerzy Chojnacki
    View author publications

    Search author on:PubMed Google Scholar

  3. Leon Kukiełka
    View author publications

    Search author on:PubMed Google Scholar

  4. Jan Najser
    View author publications

    Search author on:PubMed Google Scholar

  5. Jan Kielar
    View author publications

    Search author on:PubMed Google Scholar

  6. Tomáš Najser
    View author publications

    Search author on:PubMed Google Scholar

  7. Leszek Sergiel
    View author publications

    Search author on:PubMed Google Scholar

Contributions

B.B., J.C., J.N., and L.S. research concept; B.B., J.C., J.N., J.K., and T.N. methodology development; B.B., J.C., L.K., and T.N. data analysis; B.B., J.C., L.K., J.K., and L.S. wrote the manuscript text; J.C., L.K., and T.N. prepared figures; L.K., J.N., J.K., and L.S. formal analysis.

Corresponding author

Correspondence to
Jerzy Chojnacki.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Berner, B., Chojnacki, J., Kukiełka, L. et al. Plant spraying quality when used by drone-robots.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-40649-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-40649-6

Keywords

  • UAVs
  • Environmental protection
  • Food and energy crops
  • Air flow
  • Spray deposition
  • Leaf area index (LAI)

Source: Ecology - nature.com

A novel hybrid model for species distribution prediction of soil-transmitted helminthiasis (STH) under soil temperature conditions using Random Forest and Particle Swarm Optimization Algorithm

A miniature, subterranean, blind cobitid loach, Gitchak nakana, new genus and species, is the first groundwater-dwelling fish from Northeast India

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close