Scaling Farm Autonomy Decentralized Control, Data, and Edge

This review examines the landscape of decentralized agricultural automation systems, motivated by the need to synthesize findings from a rapidly evolving field. It addresses the shift from traditional centralized approaches towards more scalable, robust, and privacy-preserving solutions in modern farming. The review's scope focuses on three distinct yet interconnected layers of decentralization: decentralized control (Layer A), involving swarm and multi-agent robotics; decentralized data/analytics (Layer B), focusing on blockchain and federated learning; and distributed hardware/edge IoT (Layer C), centered on edge-first frameworks and on-device AI. Key findings indicate that Layer A is a maturing field with significant algorithmic progress, alongside increasing field prototypes demonstrating improved coverage and resilience. Layer B shows rapid growth in hybrid designs that combine privacy-preserving federated learning with blockchain for auditability and decentralized aggregation. Layer C highlights the emergence of edge-first frameworks and agentic AI on devices for low-latency perception and action. Common challenges across all layers include connectivity limitations in rural areas, data heterogeneity, establishing trust and incentive mechanisms, and managing energy constraints. The ultimate aim is to inform the development and deployment of robust and scalable decentralized agricultural automation solutions.