World Model–Enhanced Autonomous Agricultural Drone Swarms for Long-Horizon Coverage Planning and Precision Crop Protection
Keywords:
world models, autonomous drones, swarm intelligence, coverage planning, precision agriculture, crop protection, long-horizon planning, multi-UAV systems, governance, sustainabilityAbstract
The integration of world models into autonomous agricultural drone swarms represents a transformative paradigm for addressing the dual challenges of long-horizon coverage planning and precision crop protection. While existing multi-agent systems for agricultural monitoring and spraying rely on reactive control or short-term optimization, they often fail to anticipate future environmental dynamics, resource constraints, and crop health trajectories. This paper proposes a system-level architecture in which a centralized or distributed world model—informed by historical data, real-time sensor streams, and crop growth models—enables a swarm of unmanned aerial vehicles to engage in predictive planning over extended temporal horizons. The paper examines structural trade-offs between computational complexity and decision latency, the role of shared latent representations in coordinating heterogeneous swarm behaviors, and the governance of autonomous operations across large irregular farmlands. Emphasis is placed on the interplay between coverage completeness, pesticide application precision, and environmental sustainability. The analysis further addresses robustness against sensor failures, adversarial weather conditions, and communication intermittency, as well as fairness in resource allocation across fields with varying topographies. Policy implications include the need for regulatory frameworks that certify world model fidelity, data sovereignty for farm operators, and liability structures for autonomous decisions. By linking advances in deep reinforcement learning, model-based planning, and swarm intelligence, this work outlines a coherent research agenda for next-generation agricultural autonomy that balances productivity, ecological stewardship, and socio-technical resilience.
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