Intelligent Drone Swarm Coordination for Precision Farming and Disaster Response
DOI:
https://doi.org/10.64137/31079377/IJMSD-V2I2P103Keywords:
Drone Swarms, Swarm Intelligence, Precision Agriculture, Disaster Response, Decentralized Coordination, Bio-Inspired OptimizationAbstract
This study explores how reliable intelligent drone swarms are utilized as a single efficient bi-functional agent for precision agriculture and disaster relief support. Even though autonomous Unmanned Aerial Vehicles (UAVs) change individual sectors, traditional systems with one agent face limits in operational scale, battery duration as well as fault tolerance. This paper proposes a resilient co-design architecture based on decentralized swarm intelligence, bio-inspired optimization algorithms alongside dynamic ad-hoc networking capable of seamlessly transitioning from agricultural monitoring to search-and-rescue missions in minutes. We propose a hybrid coordination scheme, integrating Modified Particle Swarm Optimization (MPSO) and decentralized consensus protocols, allowing for near real-time avoidance of obstacles, task allocation and on-the-fly area coverage based on limited communication mapping in unstructured environments. HIL simulations and field validations over diff operational topologies show that the suggested system can decrease overall mission time by 32% and increase coverage efficiency by 24% compared to conventional centralized routing algorithms. Also, the swarm showed substantial self-healing properties even after massive nodes loss 30% active nodes) still providing 88% of operational throughput. In the end, this renewable energy and nutrient-cycling infrastructure provides an economically competitive, high-performance solution for rural resilience and resource management that reconciles the dual rescue technologies of contemporary commercial food production with advanced emergency crisis technologies.
References
[1] Han-Lim Choi, L. Brunet, and J. P. How, “Consensus-Based Decentralized Auctions for Robust Task Allocation,” IEEE Transactions on Robotics, vol. 25, no. 4, pp. 912–926, Aug. 2009, doi: https://doi.org/10.1109/tro.2009.2022423.
[2] M. Erdelj, M. Król, and E. Natalizio, “Wireless sensor networks and drones for disaster management,” Computer Networks, vol. 124, pp. 72-86, 2017.
[3] “Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948. - References - Scientific Research Publishing,” www.scirp.org. https://www.scirp.org/reference/referencespapers?referenceid=1847917
[4] T. T. Mac, C. Copot, D. T. Tran, and R. D. Keyser, “A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization,” Applied Soft Computing, vol. 59, pp. 68–76, Oct. 2017, doi: https://doi.org/10.1016/j.asoc.2017.05.012.
[5] P. Radoglou-Grammatikis et al., “Aerial surveillance and edge computing for smart farming: A review,” Computers and Electronics in Agriculture, vol. 174, 2020.
[6] Z. Xu et al., “Efficient multi-UAV path planning in dynamic and complex environments using hybrid polar lights optimization,” Journal of King Saud University - Computer and Information Sciences, vol. 37, no. 6, Jul. 2025, doi: https://doi.org/10.1007/s44443-025-00139-7.
[7] P. Tokekar, J. V. Hook, D. Mulla, and V. Isler, “Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1498–1511, Dec. 2016, doi: https://doi.org/10.1109/tro.2016.2603528.
[8] C. Zhang and J. M. Kovacs, “The application of small unmanned aerial systems for precision agriculture: a review,” Precision Agriculture, vol. 13, no. 6, pp. 693–712, Jul. 2012, doi: https://doi.org/10.1007/s11119-012-9274-5.
