Federated Learning-Based Multi-UAV Collaboration for Adaptive Pest Detection and Precision Spraying in Smart Agriculture
Keywords:
Federated Learning, Multi-UAV Collaboration, Precision Agriculture, Pest Detection, Smart Spraying, Adaptive SystemsAbstract
The integration of unmanned aerial vehicles into precision agriculture has created new opportunities for scalable pest monitoring and targeted chemical application. However, the reliance on centralized data aggregation for training deep learning models compromises data sovereignty, increases communication overhead, and introduces latency that is incompatible with real-time field decisions. This paper proposes a federated learning-based multi-UAV collaboration framework that enables distributed pest detection and adaptive spraying without requiring raw imagery to leave individual farm nodes. The architecture decouples local model training on each UAV or edge gateway from a global model aggregation server, thereby preserving privacy, reducing bandwidth consumption, and allowing the system to adapt to heterogeneous agro-ecological conditions. We examine the structural trade-offs inherent in this design, including the tension between model convergence speed and communication efficiency under constrained wireless links, the impact of non-independent and identically distributed (non-IID) data across farms on global model accuracy, and the need for Byzantine-robust aggregation mechanisms. The paper also addresses infrastructure requirements such as edge computing resources, cellular or satellite backhaul, and energy-aware mission scheduling. Beyond technical aspects, we discuss governance challenges related to data ownership, fairness of pest risk assessment across smallholders versus large agribusinesses, and policy frameworks for shared model liability. Cross-domain comparisons with federated learning in healthcare and autonomous driving are drawn to illuminate generalizable principles. A case illustration of irregular farmland spraying demonstrates how the required coordination can be achieved through swarm-inspired path planning. The analysis reveals that federated learning offers a viable pathway toward resilient, equitable, and sustainable precision spraying operations, provided that system architects carefully balance local autonomy with global coherence.
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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.
