Digital Twin–Enabled Cooperative Path Planning and Resource Optimization for Large-Scale Agricultural UAV Fleets
Keywords:
digital twin, precision agriculture, multi-UAV systems, cooperative path planning, resource optimization, swarm intelligence, fleet management, cyber-physical systems, sustainability, governanceAbstract
The increasing adoption of unmanned aerial vehicles in precision agriculture has created an urgent need for coordinated path planning and resource allocation across large-scale fleets. This paper proposes a digital twin–enabled framework that integrates real-time simulation, cooperative control, and adaptive resource optimization to improve the efficiency, sustainability, and fairness of agricultural UAV operations. Drawing on advances in cyber-physical systems and multi-agent coordination, the framework constructs a continuously updated virtual replica of the physical fleet, crop fields, weather conditions, and operational constraints. The digital twin enables predictive scenario analysis, conflict resolution, and dynamic replanning, while a cooperative path planning layer uses swarm intelligence and consensus protocols to generate collision-free, energy-efficient trajectories for hundreds of heterogeneous UAVs. Resource optimization is addressed through a multi-objective approach that balances mission completion time, battery consumption, pesticide or fertilizer application accuracy, and fleet longevity. The paper further examines the architectural trade-offs between centralized and decentralized control, the role of edge and cloud computing in ensuring low-latency synchronization, and the governance challenges associated with autonomous decision-making in agricultural contexts. Sustainability implications are analyzed through life-cycle assessments and robustness metrics, while fairness considerations are discussed in terms of equitable resource distribution among fields of varying sizes and ownership structures. Policy recommendations are offered for regulatory frameworks that can accommodate dynamic fleet operations without compromising safety or environmental standards. The findings demonstrate that digital twin–enabled coordination can substantially reduce energy waste, improve coverage uniformity, and enhance overall system resilience. This research contributes to the growing literature on intelligent agricultural infrastructure and provides a blueprint for deploying large-scale autonomous fleets in a socially responsible and environmentally sustainable manner.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
