Effects of Optimizing Droplet Distribution at Particular Heights and Speeds Using Proportional-Integral-Derivative (PID) Control Algorithm in Unmanned Aerial Vehicle (UAV) Systems: A Review

Abstract

Unmanned aerial vehicles (UAVs) are increasingly used in agriculture to increase productivity, optimize resources, and ensure environmental sustainability. This study investigates the droplet distribution of UAVs in agricultural spraying and examines the effects of flight altitude and speed parameters. Experiments conducted on various plant species and tree structures demonstrate that these parameters play acrucial role in ensuring uniform droplet deposition and reducing pesticide use. Concrete recommendations are given to optimize UAV systems in agricultural spraying applications. The paper focuses specifically on the role of the Proportional-Integral-Derivative (PID) control algorithm in improving spray parameters. It evaluates the effects of flight speed and altitude on droplet density and uniformity. A systematic literature review and analysis of experimental data support the methodology presented. The results demonstrate that the PID algorithm outperforms uncontrolled systems. This review synthesizes the existing literature to highlight the effectiveness of UAV-based spraying systems in terms of agricultural sustainability and opportunities for future research.

Keywords

• PID controller
• Unmanned aerial vehicle
• Control algorithm
• Droplet distribution
• Agricultural sustainability
• Optimization

References

1. Adi P D P, Mustamu N E, Siregar V M & Sihombing V (2021). Drone simulation for agriculture and LoRa based approach. Internet of Things and Artificial Intelligence Journal 1(4): 221–235. https://doi.org/10.31763/iota.v1i4.501
2. Adesanya M A, Obasekore H, Rabiu A, Na W-H, Ogunlowo Q O, Akpenpuun T D, Kim M-H, Kim H-T, Kang B-Y & Lee H-W (2024). Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy optimization: A TRNSYS-Python cosimulation approach. Expert Systems with Applications, 252(Part A), 124126. https://doi.org/10.1016/j.eswa.2024.124126
3. Ahı Koşar D, Sönmez E, Argaç A, Ertürk U (2023). An Unmanned aerial vehicle based artificial pollination in a frost-affected walnut (Juglans regia L.) Orchard. Journal of Agricultural Sciences 29(3): 765-776. https://doi.org/10.15832/ankutbd.1163150
4. Alkan B & Özgünaltay Ertuğrul G (2022). Agricultural unmanned aerial vehicle pesticide applications. Journal of the Faculty of Agriculture, Ahi Evran University, Kırşehir 2(2): 232–238 (In Turkish)
5. Amoresano A, Langella G, Iodice P & Roscioli S (2023). Numerical analysis of SO2 absorption inside a single water drop. Atmosphere 14(12): 1746. https://doi.org/10.3390/atmos14121746
6. Åström K J & Murray R M (2010). Feedback Systems: An introduction for scientists and engineers (2nd ed.). Princeton University Press.
7. Aparco R H, Tapia-Tadeo F, Ascarza Y B (2024). A machine learning approach for analyzing residual stress distribution in cold spray coatings. J Therm Spray Tech 33: 1292–1307. https://doi.org/10.1007/s11666-024-01776-6
8. Ayamga M, Tekinerdogan B & Kassahun A (2021). Exploring the challenges posed by regulations for the use of drones in agriculture in the african context. Land 10(2): 164. https://doi.org/10.3390/land10020164

9. Bahnam B S, Dawwod S A & Younis M C (2024). Optimizing software reliability growth models through simulated annealing algorithm: Parameters estimation and performance analysis. Journal of Supercomputing 80: 16173–16201. https://doi.org/10.1007/s11227-02406046-4
10. Biswas S, Pandey R, Dhakal S & Gowtham M (2023). Surveillance and inspection micro quadcopter drone in agriculture. International Research Journal of Modernization in Engineering Technology and Science 5(9). https://doi.org/10.56726/irjmets44985
11. Borikar G P, Gharat C & Deshmukh S R (2022b). Application of drone systems for spraying pesticides in advanced agriculture: A Review. IOP Conference Series Materials Science and Engineering 1259(1): 012015. https://doi.org/10.1088/1757-899x/1259/1/012015
12. Bunkovsky V (2023). Analysis of the innovative development of the agro-industrial complex. BIO Web of Conferences 71: 01037. https://doi.org/10.1051/bioconf/20237101037
13. Yin H, (2013). A fuzzy controller based on incomplete differential ahead PID algorithm for a remotely operated vehicle. Ocean Systems Engineering 3(3): 237–255. https://doi.org/10.12989/ose.2013.3.3.237
14. Changyuan Z, Chunjiang Z, Xiu W, Wei L & Ruixiang Z (2014). Nozzle test system for droplet deposition characteristics of orchard air assisted sprayer and its application. International Journal of Agricultural and Biological Engineering 7(2): 122–129. https://doi.org/10.25165/ijabe.v7i2.1216
15. Chao C, Sutarna N, Chiou J & Wang C (2017). Equivalence between fuzzy PID controllers and conventional PID controllers. Applied Sciences 7(6): 513. https://doi.org/10.3390/app7060513
16. Chao C, Sutarna N, Chiou J & Wang C (2019). An optimal fuzzy PID controller design based on conventional control and nonlinear factors. Applied Sciences 9(6): 1224. https://doi.org/10.3390/app9061224 Chen P, Douzals J P, Lan Y, Cotteux E, Delpuech X, Pouxviel G & Zhan Y (2022). Characteristics of unmanned aerial spraying systems and related spray drift: A review. Frontiers in Plant Science 13. https://doi.org/10.3389/fpls.2022.870956
17. Chen S, Lan Y, Zhou Z, Ouyang F, Wang G, Huang X, Deng X & Cheng S (2020). Effect of droplet size parameters on droplet deposition and drift of aerial spraying by using plant protection UAV. Agronomy 10(2): 195. https://doi.org/10.3390/agronomy10020195
18. Chojnacki J & Pachuta A (2021). Impact of the parameters of spraying with a small unmanned aerial vehicle on the distribution of liquid on young cherry trees. Agriculture 11(11) 1094. https://doi.org/10.3390/agriculture11111094
19. Coffeng L E, de Vlas S J, Singh R P, James A, Bindroo J, Sharma N K, Ali A, Singh C, Sharma S & Coleman M (2024). Effect of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India 2016–22: an interrupted time-series analysis and modelling study. In The Lancet Infectious Diseases (Vol. 24, Issue 11, pp. 1266–1274). Elsevier BV. https://doi.org/10.1016/s1473 3099(24)00420-1
20. Costopoulou C (2022). Intention to use drones in agriculture: Evidence from Greece. Modern Concepts & Developments in Agronomy 10(3): https://doi.org/10.31031/mcda.2022.10.000736
21. Desa H, Azizan M A, Zulkepli N N, Ishak N, Hang T X, Yahya S S, Shahrazel A a M, Mansor F M, Aziz S Z A & Hussain A T (2023). Effect of spraying dispersion using UAV spraying system with different height at paddy field. Proceedings of International Conference on Artificial Life and Robotics, 28: 573–579. https://doi.org/10.5954/icarob.2023.os24-1
22. Du H, Liu P, Cui Q, Ma X & Wang H (2022). PID controller parameter optimized by reformative artificial bee colony algorithm. Journal of Mathematics 2022(1). https://doi.org/10.1155/2022/3826702
23. D’Orbcastel E R, Blancheton J & Aubin J (2009). Towards environmentally sustainable aquaculture: Comparison between two trout farming systems using Life Cycle Assessment. Aquacultural Engineering 40(3): 113–119. https://doi.org/10.1016/j.aquaeng.2008.12.002Elajrami
24. M, Satla Z & Bendine K (2021). Drone control using the coupling of the PID controller and genetic algorithm. Communications - Scientific Letters of the University of Zilina 23(3): C75–C82. https://doi.org/10.26552/com.c.2021.3.c75-c82
25. Guo H, Zhou J, Liu F, He Y, Huang H & Wang H (2020). Application of machine learning method to quantitatively evaluate the droplet size and deposition distribution of the UAV spray nozzle. Applied Sciences 10(5): 1759. https://doi.org/10.3390/app10051759
26. Guo S, Li J, Yao W, Zhan Y, Li Y & Shi Y (2019). Distribution characteristics on droplet deposition of wind field vortex formed by multi rotor UAV. PLoS ONE, 14(7), e0220024. https://doi.org/10.1371/journal.pone.0220024 Güven A & Koç İ (2020). Changes in non-target nematode, bacterial, and microfungal populations in the soil after certain pesticide applications. Journal of Agricultural Sciences of Yüzüncü Yıl University 30(2): 252–265. https://doi.org/10.29133/yyutbd.689385 (In Turkish)
27. Hanif A S, Han X & Yu S (2022). Independent control spraying system for UAV-based precise variable sprayer: A review. Drones 6(12): 383. https://doi.org/10.3390/drones6120383
28. Hao Z, Li X, Meng C, Yang W & Li M (2022). Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning. International Journal of Agricultural and Biological Engineering 15(4): 16–26. https://doi.org/10.25165/j.ijabe.20221504.6929
29. Hewitt A, Chen L, Li L & Tang Q (2023). Challenges and opportunities of unmanned aerial vehicles as a new tool for crop pest control. Pest Management Science 79(11): 4123-4131. https://doi.org/10.1002/ps.7683
30. Hou C, Tang Y, Luo S, Lin J, He Y, Zhuang J & Huang W (2019). Optimization of control parameters of droplet density in citrus trees using UAVs and the Taguchi method. International Journal of Agricultural and Biological Engineering 12(4): 1–9. https://doi.org/10.25165/j.ijabe.20191204.4139
31. Hu H, Kaizu Y, Huang J, Furuhashi K, Zhang H, Li M & Imou K (2022). Research on methods decreasing pesticide waste based on plant protection unmanned aerial vehicles: A review. Frontiers in Plant Science 13. https://doi.org/10.3389/fpls.2022.811256
32. Hu J, Wang T, Yang J, Lan Y, Lv S & Zhang Y (2020). WSN-assisted UAV trajectory adjustment for pesticide drift control. Sensors 20(19): 5473. https://doi.org/10.3390/s20195473
33. Hu W & Shorinov O (2024). Optimization of particle acceleration parameters of special cold spray nozzles via neural network and genetic algorithm. In Aerospace Technic and Technology (Issue 4, pp. 64–70). National Aerospace University - Kharkiv Aviation Institute. https://doi.org/10.32620/aktt.2024.4.08
34. Huang M, Tian M, Liu Y, Zhang Y & Zhou J (2022). Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm. Scientific Reports 12(1). https://doi.org/10.1038/s41598-022-16425-7
35. Hussain Saddam & Cheema, Muhammad Jehanzeb & Arshad, Muhammad & Chatta, ashfaq & Latif, Muhammad & Ashraf, Shaharyar & Siddique, Shoaib. (2019). Spray uniformity testing of unmanned aerial spraying system for precise agro-chemical applications. Pakistan Journal of Agricultural Sciences. 56. 10.21162/PAKJAS/19.8594.
36. Huynh N & Nguyen K (2024). Real-time droplet detection for agricultural spraying systems: A deep learning approach. Machine Learning and Knowledge Extraction 6(1): 259–282. https://doi.org/10.3390/make6010014
37. Inan M & Karci A (2021). Development and implementation of a new method for tree spraying in agriculture using drones. Computer Science, 6(2): 72–89. https://dergipark.org.tr/en/pub/bbd/issue/62530/928229 (In Turkish) for agricultural Ismail S A, Yahya A, Su A S M, Asib N & Mustafah A M (2021). Drone payload and flying speed effects on rotor blades’ rpm and traveling pattern chemical spraying. Basrah Journal of Agricultural Sciences 34: 157–170. https://doi.org/10.37077/25200860.2021.34.sp1.16
38. Ivezić A, Trudić B, Stamenković Z, Kuzmanović B, Perić S, Ivošević B, Buđen M & Petrović K (2023). Drone-related agrotechnologies for precise plant protection in western balkans: applications, possibilities, and legal framework limitations. Agronomy, 13(10): 2615. https://doi.org/10.3390/agronomy13102615
39. Jayakumar V, Mohideen A B K, Saeed M H, Alsulami H, Hussain A & Saeed M (2023b). Development of complex linear diophantine fuzzy soft in determining a suitable agri-drone for spraying fertilizers and pesticides. IEEE Access, 11: 9031–9041. https://doi.org/10.1109/access.2023.3239675
40. Jeong J Y, Byun J W & Jeong I R (2022). Key agreement between user and drone with forward unlinkability in internet of drones. IEEE Access 10: 17134–17144. https://doi.org/10.1109/access.2022.3150035
41. Jiang Y, He X, Song J, Liu Y, Wang C, Li T, Qi P, Yu C & Chen F (2022). Comprehensive assessment of intelligent unmanned vehicle techniques in pesticide application: A case study in pear orchard. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.959429
42. Jigang H, Jie W & Hui F (2017). An anti-windup self-tuning fuzzy PID controller for speed control of brushless DC motor. Automatika 58(3): 321–335. https://doi.org/10.1080/00051144.2018.1423724
43. Jin L, Fan J, Du F & Zhan M (2023). Research on two-stage semi-active ISD suspension based on improved fuzzy neural network PID control. Sensors 23(20): 8388. https://doi.org/10.3390/s23208388
44. Karabey A, Özkan Y, Sayinci B & Yeşı̇ Ldal F (2020). Determination of spray characteristics under optimized conditions for pesticide applications. Journal of the Institute of Science, Gümüşhane University. https://doi.org/10.17714/gumusfenbil.707891 (In Turkish)
45. Khanmohammadi S, Khanjani S & Hashemi N (2024). Experimental and analytical examinations of a single-glazed solar still desalination with a spray-feeding water system with an artificial neural network. In Applied Thermal Engineering (Vol. 249, p. 123408). Elsevier BV. https://doi.org/10.1016/j.applthermaleng.2024.123408
46. Khather S I, Ibrahim M A & Ibrahim M H (2023). Dual fuzzy logic PID controller based regulating of dc motor speed control with optimization using Harmony Search algorithm. Eastern-European Journal of Enterprise Technologies 4(8 (124)): 6–14. https://doi.org/10.15587/1729 4061.2023.282830
47. Kim S K, Ahmad H, Moon J W & Jung S Y (2021). Nozzle with a feedback channel for agricultural drones. Applied Sciences 11(5): 2138. https://doi.org/10.3390/app11052138
48. Kina E (2025). TLEABLCNN: Brain and Alzheimer’s disease detection using attention-based explainable deep learning and SMOTE using imbalanced brain MRI. IEEE Access 13: 27670–27683.
49. Kjær C, Bruus M, Bossi R, Løfstrøm P, Andersen H V, Nuyttens D & Larsen S E (2014). Pesticide drift deposition in hedgerows from multiple spray swaths. Nippon Nōyaku Gakkaishi, 39(1): 14–21. https://doi.org/10.1584/jpestics.d12-045
50. Konar G, Mandal K K & Chakraborty N (2014). Two area load frequency control of hybrid power system using genetic algorithm and differential evolution tuned PID controller in deregulated environment. In Springer eBooks (pp. 263–278). https://doi.org/10.1007/978-94 017-9588-3_21
51. Lalitha K, Suresh M X, Selvakumar R A P, Sunitha J M & Meenakshi B (2024). Sustainable crop protection using IoT-enabled drone spraying with support vector machine analysis. In Proceedings of the 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 412–418). IEEE. https://doi.org/10.1109/ICoICI62503.2024.10696038
52. Lan Y, Qian S, Chen S, Zhao Y, Deng X, Wang G, Zang Y, Wang J & Qiu X (2021). Influence of the downwash wind field of plant protection UAV on droplet deposition distribution characteristics at different flight heights. Agronomy 11(12): 2399. https://doi.org/10.3390/agronomy11122399
53. Li X, Giles D K, Niederholzer F J, Andaloro J T, Lang E B & Watson L J (2020). Evaluation of an unmanned aerial vehicle as a new method of pesticide application for almond crop protection. Pest Management Science 77(1): 527–537. https://doi.org/10.1002/ps.6052
54. Lin E, Li J, Xing C, Wang B & Zhang J (2025). Dynamic behavior investigation of zinc vapor in the vacuum spray galvanizing process based on the direct simulation Monte Carlo method. In Applied Thermal Engineering (Vol. 266, p. 125719). Elsevier BV. https://doi.org/10.1016/j.applthermaleng.2025.125719 Lin R (2023). Research on manipulator control based on improved PID algorithm (p. 81). https://doi.org/10.1117/12.2684762
55. Liu G, Sun J & Dong W (2021). “Parameter setting of improved particle swarm optimization algorithm in building energy consumption optimization”, Journal of Tianjin University (Natural Science and Engineering), Vol. 54 No. 1, pp. 82-90
56. Liu Q, Chen S, Wang G & Lan Y (2021). 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. Applied Sciences 11(16): 7258. https://doi.org/10.3390/app11167258
57. Liu X, Guo J & Im H G (2023). Development of correlation model for cavitating spray using Eulerian simulations. In International Journal of Engine Research (Vol. 25, Issue 4, pp. 613–630). SAGE Publications. https://doi.org/10.1177/14680874231200759
58. Liu Y, Xiao Q, Han X, Zeeshan M, Fang Z & Dou Z (2022). Effect of aerial application of adjuvants on pepper defoliant droplet deposition and efficacy of defoliation sprayed by unmanned aerial vehicles. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.917462
59. Luna M A, Isaac M S A, Ragab A R, Campoy P, Peña P F & Molina M (2022). Fast multi-UAV path planning for optimal area coverage in aerial sensing applications. Sensors 22(6): 2297. https://doi.org/10.3390/s22062297
60. Martin D E, Woldt W E & Latheef M A (2019). Effect of application height and ground speed on spray pattern and droplet spectra from remotely piloted aerial application systems. Drones 3(4): 83. https://doi.org/10.3390/drones3040083 Meng Y, Saito H & Bernard C (2024). Optimal design of a cold spray nozzle for inner wall coating fabrication by combining CFD simulation and neural networks. Journal of Thermal Spray Technology 33(1): 3–16. https://doi.org/10.1007/s11666-024-01716-4
61. Meng Y, Su J, Song J, Chen W & Lan Y (2020). Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution. Computers and Electronics in Agriculture, 170, 105282. https://doi.org/10.1016/j.compag.2020.105282 Nascimento V P D & Da Vitória E L (2022). Spraying quality using unmanned aerial vehicle in citrus. Revista Engenharia Na Agricultura - REVENG 30: 214–221. https://doi.org/10.13083/reveng.v30i1.13700
62. Nasim S, Rashid M, Syed S A & Brohi I (2023). Intelligent agricultural pest manager drone in Pakistan. Pakistan Journal of Biotechnology, 20(02): 238–242. https://doi.org/10.34016/pjbt.2023.20.02.816
63. Nordin M N, Jusoh M S M, Bakar B H A, Ahmad M T, Mail M F, Vun C T, Chuang T C, Basri M S H & Zolkafli A K (2021). Study on water distribution of spraying drone by different speed and altitude. Advances in Agricultural and Food Research Journal. https://doi.org/10.36877/aafrj.a0000215
64. Ogundari K & Bolarinwa O D (2018). Impact of agricultural innovation adoption: a meta‐analysis. Australian Journal of Agricultural and Resource Economics 62(2): 217–236. https://doi.org/10.1111/1467-8489.12247
65. Ou M, Zhang J, Du W, Wu M, Gao T, Jia W, Dong X, Zhang T & Ding S (2024). Design and experimental research of air-assisted nozzle for pesticide application in orchard. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1405530
66. Özyurt H B, Duran H & Çelen İ H (2022). Determination of spraying drone application parameters for crop production in hazelnut Orchards. Journal of Tekirdağ Faculty of Agriculture 19(4): 819–828. https://doi.org/10.33462/jotaf.1105420 (In Turkish) Pandey, Raviraj & Biswas, Sourav & Lawrence I (2023). Dataveillance management using micro drone technology for agriculture purpose in Nepal. International Research Journal of Modernization in Engineering Technology and Science 05. 2582-5208. 10.56726/IRJMETS44988.
67. Privitera S, Manetto G, Pascuzzi S, Pessina D & Cerruto E (2023). Drop size measurement techniques for agricultural sprays: A state-of-the art review. Agronomy 13(3): 678. https://doi.org/10.3390/agronomy13030678 mildew.
68. Qin W, Xue X, Zhang S, Gu W & Wang B (2018). Droplet deposition and efficiency of fungicides sprayed with small UAV against wheat powdery International Journal of Agricultural and Biological Engineering 11(2): 27–32. https://doi.org/10.25165/j.ijabe.20181102.3157
69. Raju V R V, Varma S N, Vangari P, Kumar S, Joshi K & Penta S (2023). Integrating aerial electric vehicles for sustainable agriculture and to optimize the overhead cost of farming. E3S Web of Conferences, 430, 01003. https://doi.org/10.1051/e3sconf/202343001003
70. Ruiz M C, Bloise N, Guglieri G & D’Ambrosio D (2022). Numerical analysis and wind tunnel validation of droplet distribution in the wake of an unmanned aerial spraying system in forward flight. Drones 6(11): 329. https://doi.org/10.3390/drones6110329
71. Salyani M, Zhu H, Sweeb R D & Pai N (2013). Assessment of spray distribution with water-sensitive paper. Agricultural Engineering International: CIGR Journal, 15(2): 101–111. https://www.cigrjournal.org/index.php/Ejounral/article/view/2439
72. Samseemoung G, Bhucksasri J, Parnsakhorn S, Kalsirisilp R, Samseemung M & Jayasuriya H (2023). Comparison of drone with remote controlled sprayer arm and variable rate sprayer for monitoring coconut rhinoceros beetle infestations. Agriculture and Natural Resources, 57(2). https://doi.org/10.34044/j.anres.2023.57.2.05
73. Sayinci B, Demı̇ R B & Açik N (2019). Estimation of droplet density and spray characteristics in sprayer nozzles. Journal of Agricultural Sciences of Yüzüncü Yıl University 29(3): 458–465. https://doi.org/10.29133/yyutbd.573698
74. Sağlam R, Kılıç T & Tobi İ (2014). A study on the determination of droplet distribution and application effectiveness in sunn pest control using agricultural aircrafts. Harran Journal of Agricultural and Food Science 14(3): 47–53 (In Turkish)
75. Sebastiao A, Lucena C, Palma L, Cardoso A & Gil P (2015). Optimal tuning of scaling factors and membership functions for mamdani type PID fuzzy controllers. 2015 IEEE International Conference on Control, Automation and Robotics (ICCAR), 92–96. https://doi.org/10.1109/iccar.2015.7166009
76. Shi X & Zhu C (2022). Research on trajectory planning and control of operational underwater robots. Mathematical Problems in Engineering, 2022, 1–11. https://doi.org/10.1155/2022/1986425
77. Singh E, Pratap A, Mehta U & Azid S I (2024). Smart agriculture drone for crop spraying using image-processing and machine learning techniques: Experimental validation. IoT 5(2): 250-270. https://doi.org/10.3390/iot5020013
78. Siwek M, Baranowski L & Ładyżyńska-Kozdraś E (2024). The Application and optimisation of a neural Network PID controller for trajectory tracking using UAVs. Sensors 24(24): 8072. https://doi.org/10.3390/s24248072
79. Skvortsova T, Pratsko G, Isakova Y & Marchenko E (2023). State policy of ussia in the field of transition to an innovative economy in agribusiness. E3S Web of Conferences 371, 01072. https://doi.org/10.1051/e3sconf/202337101072
80. Song L, Huang J, Liang X, Yang S X, Hu W & Tang D (2020). An intelligent multi-sensor variable spray system with chaotic optimization and adaptive fuzzy control. Sensors 20(10): 2954. https://doi.org/10.3390/s20102954
81. Soundirarrajan N & Srinivasan K (2019). Performance evaluation of ant lion optimizer–based PID controller for speed control of PMSM. Journal of Testing and Evaluation 49(2): 1104–1118. https://doi.org/10.1520/jte20180892
82. Sun Y, Vegad C S, Li Y, Renou B, Nishad K, Demoulin F-X, Wang W, Hasse C & Sadiki A (2025). Evaluation of turbulent co-flow effects on liquid fuel atomization including spray evolution from a pressure swirl atomizer. In International Journal of Multiphase Flow (Vol. 184, p. 105100). Elsevier BV. https://doi.org/10.1016/j.ijmultiphaseflow.2024.105100
83. Sya’roni I, Sutopo W & Rochani R (2023). The Influence of technopreneur ship and innovation systems on the adoption of agricultural drones in Indonesia. Proceedings of the 4th Asia Pacific Conference on Industrial Engineering and Operations Management Ho Chi Minh City. https://doi.org/10.46254/ap04.20230065
84. Taseer A & Han X (2024). Advancements in variable rate spraying for precise spray requirements in precision agriculture using unmanned aerial spraying systems: A review. Computers and Electronics in Agriculture, 219, 108841. https://doi.org/10.1016/j.compag.2024.108841
85. Ukaegbu U F, Tartibu L K, Okwu M O & Olayode I O (2021). Development of a light-weight unmanned aerial vehicle for precision agriculture. Sensors 21(13): 4417. https://doi.org/10.3390/s21134417
86. Van Der Merwe D, Burchfield D R, Witt T D, Price K P & Sharda A (2020). Drones in agriculture. In Advances in agronomy (pp. 1–30). https://doi.org/10.1016/bs.agron.2020.03.001
87. Vitória E L d, Krohling C A, Borges F R P, Ribeiro L F O, Ribeiro M E A, Chen P, Lan Y, Wang S, Moraes H M F e & Furtado Júnior M R (2023). Efficiency of fungicide application an using an unmanned aerial vehicle and pneumatic sprayer for control of hemileia vastatrix and cercospora coffeicola in mountain coffee crops. Agronomy 13(2): 340. https://doi.org/10.3390/agronomy13020340
88. Wang B, Zhang Y, Wang C & Teng G (2022). Droplet deposition distribution prediction method for a six-rotor plant protection UAV based on inverse distance weighting. Sensors 22(19): 7425. https://doi.org/10.3390/s22197425
89. Wang C-H, Pan Q-K, Li X-P, Sang H-Y & Wang B (2024). A multi-objective teaching-learning-based optimizer for a cooperative task allocation problem of weeding robots and spraying drones. In Swarm and Evolutionary Computation (Vol. 87, p. 101565). Elsevier BV. https://doi.org/10.1016/j.swevo.2024.101565
90. Wang G, Lan Y, Qi H, Chen P, Hewitt A & Han Y (2019). Field evaluation of an unmanned aerial vehicle (uav) sprayer: effect of spray volume on deposition and the control of pests and disease in wheat. Pest Management Science 75(6): 1546-1555. https://doi.org/10.1002/ps.5321
91. Wang J, Lan Y, Wen S, Hewitt A J Yao W & Chen P (2020). Meteorological and flight altitude effects on deposition, penetration, and drift in pineapple aerial spraying. Asia-Pacific Journal of Chemical Engineering 15(1). https://doi.org/10.1002/apj.2382 Wang Z, Zhang Y, Li T, Müller J & He X (2023). Visualization of lidar-based 3D droplet distribution detection for air-assisted spraying. AgriEngineering 5(3): 1136-1146. https://doi.org/10.3390/agriengineering5030072
92. Wei X, XianYu W, Jiazhen L & Yasheng Y (2023). Design of anti-load perturbation flight trajectory stability controller for agricultural UAV. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1030203
93. Wen S, Zhang Q, Deng J, Lan Y, Yin X & Shan J (2018). Design and experiment of a variable spray system for unmanned aerial vehicles based on PID and PWM control. Applied Sciences 8(12): 2482. https://doi.org/10.3390/app8122482
94. Worner S, Gevrey M, Eschen R, Kenis M, Paini D, Singh S, Watts M & Suiter K (2013) Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering. NeoBiota 18: 83-102. https://doi.org/10.3897/neobiota.18.4042
95. Xiao Y, Huo W & Nan G (2014). Research on compound pitch control technology of direct-drive permanent magnet wind turbine. The Open Electrical & Electronic Engineering Journal 8(1): 298–305. https://doi.org/10.2174/1874129001408010298
96. Xing W, Cui Y, Wang X, & Shen J (2024). Optimization of operational parameters of plant protection UAV. Sensors 24(16): 5132. https://doi.org/10.3390/s24165132
97. Xu L, Yang Z, Huang Z, Ding W & Buck-Sorlin G (2023). Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach. International Journal of Agricultural and Biological Engineering 16(1): 66–72. https://doi.org/10.25165/j.ijabe.20231601.6581
98. Vimala J, Mohideen A, Saeed M, Alsulami H, Hussain A & Saeed M (2023). Development of complex linear diophantine fuzzy soft set in determining a suitable agri-drone for spraying fertilizers and pesticides. Ieee Access 11: 9031-9041
99. Yan J G, Xi A, Xiao Z B & Xiong H X (2013). The Application of fuzzy control in position control for tanker drag. Applied Mechanics and Materials 319: 553–557. https://doi.org/10.4028/www.scientific.net/amm.319.553
100. Yang R, Li B, Dong J, Cai Z, Lin H, Wang F & Yang G (2025). Reinforcement learning-based generative artificial intelligence for novel pesticide design. In Journal of Advanced Research. Elsevier BV. https://doi.org/10.1016/j.jare.2025.02.030
101. Yu S, Kang Y & Lee C (2023). Comparison of the spray effects of air induction nozzles and flat fan nozzles installed on agricultural drones. Applied Sciences 13(20): 11552. https://doi.org/10.3390/app132011552
102. Zhai C, Zhao C, Wang X, Li W & Zhu R (2014). Nozzle test system for droplet deposition characteristics of orchard air-assisted sprayer and its application. International Journal of Agricultural and Biological Engineering 7(2): 122-129. https://doi.org/10.3965/j.ijabe.20140702.015
103. Zhang N M, Wang N J & Li D (2010). Simulation analysis of PID control system based on desired dynamic equation. Proceedings of the 2010 World Congress on Intelligent Control and Automation pp. 3638–3644. https://doi.org/10.1109/wcica.2010.5553904
104. Zhang P, Wang S, Bai M, Bai Q, Chen Z, Chen X, Hu Y, Zhang J, Li Y, Hu X, Shi Y & Deng J (2022). Intelligent spraying water based on the internet of rrchard things and fuzzy PID algorithms. Journal of Sensors 2022, 1–9. https://doi.org/10.1155/2022/4802280
105. Zhang R, Hewitt A J, Chen L, Li L & Tang Q (2023). Challenges and opportunities of unmanned aerial vehicles as a new tool for crop pest control. Pest Management Science 79(11): 4123–4131. https://doi.org/10.1002/ps.7683
106. Zhichkin K, Nosov V, Zhichkina L, Anichkina O, Borodina I & Beketov A (2023). Efficiency of using drones in agricultural production. E3S Web of Conferences 381: 01048. https://doi.org/10.1051/e3sconf/202338101048
107. Zhu H, Salyani M & Fox R D (2011). A portable scanning system for evaluation of spray deposit distribution. Computers and Electronics in Agriculture 76(1): 38–43. https://doi.org/10.1016/j.compag.2011.01.003
108. Zongo A, Badini O, Kabore E, Traore A, Sawadogo S & Sawadogo M (2023). Drone agrotechnology’s for cotton (Gossypium hirsutum L.) Protection against pest and diseases in Western of Burkina Faso. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs3496812/v1
109. Zwertvaegher I, Lamare A, Douzals J, Balsari P, Marucco P, Grella M, Caffini A, Mylonas N, Dekeyser D, Foqué D & Nuyttens D (2022). Boom sprayer optimizations for bed‐grown carrots at different growth stages based on spray distribution and droplet characteristics. Pest Management Science 78(4): 1729–1739. https://doi.org/10.1002/ps.6792