Digital Twin-Based Energy Management for Agricultural UAVs: A Survey on Power Modeling, Battery Health, and Event-Driven Decision Support
DOI:
https://doi.org/10.66395/globeis.8Keywords:
agricultural UAVs, battery state of health, digital twin, energy management, software-in-the-loop simulationAbstract
Precision agriculture increasingly relies on unmanned aerial vehicles (UAVs) for tasks such as pesticide spraying, where energy management directly determines mission safety and efficiency. This study synthesizes current digital twin-based energy management approaches for agricultural UAVs, examining four interconnected research domains: multi-rotor power consumption modeling, battery state-of-health (SoH) estimation, energy-aware flight planning, and event-driven decision support systems. Through a critical analysis of 29 sources spanning academic publications and industrial patents, we identify four fundamental research gaps: (1) the absence of dynamic payload mass reduction in energy models, (2) the lack of wind perturbation-integrated point-of-no-return (PNR) decision mechanisms, (3) insufficient integration of battery SoH into real-time energy estimation for agricultural contexts, and (4) the absence of integrated event-driven re-simulation within digital twin architectures. A cross-examination of these deficiencies reveals that existing studies address these variables in isolation, whereas their operational coupling is what determines real-world mission reliability. Accordingly, a unified digital twin framework that simultaneously integrates dynamic payload modeling, wind-aware PNR logic, SoH-informed energy estimation, and event-triggered re-simulation represents the primary unmet research need for safe autonomous agricultural UAV operations.
Downloads
References
1. Toscano, F., Lagani, R., & Musolino, G. (2023). Unmanned aerial vehicle for precision agriculture: A review. IEEE Access, 11, 10427–10451.
2. Guebsi, R., Mahmoud, H., & Zayani, S. (2024). Drones in precision agriculture: A comprehensive review. Drones, 8(3), 102.
3. Güneş, D., & Hasegawa, H. (2024). Optimizing UAV sprayer performance using field data and machine learning. Journal of Agricultural Engineering, 55(2).
4. Dihan, M. S., et al. (2024). Digital twin: Data exploration, architecture, implementation and future. Heliyon, 10(3), e26421.
5. Safaeinejad, M., et al. (2024). Reducing energy and environmental footprint: Drone spraying vs. conventional ground sprayers. Sustainability, 16(5), 1845.
6. Di Nisio, A., et al. (2023). Battery testing and discharge model validation for electric multirotor UAVs. Sensors, 23(12), 5491.
7. Schacht-Rodríguez, R., et al. (2024). Mission planning strategy for multirotor UAV based on flight endurance. IEEE Transactions on Automation Science and Engineering, 21(1), 1–13.
8. Schacht-Rodríguez, R., et al. (2024). Prognosis and health management for UAV flight endurance prediction: A review. Applied Sciences, 14(2), 1–24.
9. Hwang, M., Cha, H. R., & Jung, S. Y. (2018). Practical endurance estimation for minimizing energy consumption of multirotor UAVs. Energies, 11(5), 1218.
10. Gong, H., et al. (2024). Modelling power consumptions for multi-rotor UAVs: A physical principle-based approach. IEEE Transactions on Vehicular Technology.
11. Matras, F., et al. (2024). Modeling, analysis and optimization of multirotor power consumption considering rotor interactions. IEEE Robotics and Automation Letters, 9(4).
12. Arsalan, A., et al. (2024). Next-gen IoD: Federated learning and digital twin for energy-efficient task allocation. IEEE Access, 12, 34521–34538.
13. Escobar, L., & Pereira, G. (2024). Energy-aware coverage path planner for multirotor UAVs using mixed-integer programming. IEEE Robotics and Automation Letters, 9(2).
14. Di Franco, C., & Buttazzo, G. (2015). Energy-aware coverage path planning of unmanned aerial vehicles. In Proceedings of IEEE ICAR (pp. 17–23).
15. Hung, I.-K., et al. (2024). Assessing drone return-to-home landing accuracy in a woodland landscape. Forests, 15(1).
16. Li, H., et al. (2024). Influence of UAV wind field on pesticide droplet deposition in cotton fields. Frontiers in Plant Science, 15.
17. Nguyen, T. H., et al. (2018). Post-mission autonomous return and precision landing of UAV. In Proceedings of ICARCV (pp. 456–461).
18. Teschner, G., et al. (2022). Digital twin of drone-based protection of agricultural areas. In Proceedings of IEEE DTPI (pp. 1–4).
19. Oña, A., García, J., & Morales, R. (2025). Optimization of flight planning using digital twins and SITL simulation. Drones, 9(1).
20. Peng, C.-C., & Chen, Y.-H. (2023). Fixed-wing UAV rotary engine anomaly detection via online digital twin. IEEE Access, 11, 89102–89115.
21. Chamorro, W., et al. (2024). Open-source software-in-the-loop strategies for realistic UAV monitoring and control. IEEE Systems Journal.
22. Aliane, N. (2023). A survey of open-source UAV autopilots: Evolution and reliability. Sensors, 23(18), 7812.
23. Steindl, G., et al. (2024). Toward semantic event-handling for explainable cyber-physical systems. IEEE Access, 12, 15432–15448.
24. Zhang, Z., Jiang, J., Ling, K. V., & Zhang, W.-A. (2024). Real-time path planning for autonomous UAVs: Event-triggered multimodal adaptive pigeon-inspired optimization. IEEE Transactions on Cybernetics.
25. Chen, W., et al. (2024). Resilience-oriented real-time decision-making for multi-UAV systems. IEEE Transactions on Reliability, 73(1).
26. Qadir, M. A., et al. (2023). Digital twin-enabled UAV network for disaster management: A review. IEEE Network, 37(5), 112–119.
27. Hall, P., et al. (2023). DroNS-3: Framework for realistic drone and networking simulators. In Proceedings of ACM DroNet (pp. 25–30).
28. Yang, S. (2019). Return flight method and device for unmanned aerial vehicle in low electric quantity (WIPO Patent WO 2019/006773 A1).
29. Lei, X. (2024). Automatic return method, apparatus and unmanned aerial vehicle (U.S. Patent 11,919,637 B2). Autel Robotics.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 GlobeIS International Journal of Global Information Systems
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
