GeoAI-Driven Cooperative UAV–UGV Systems for Integrated Precision Spraying and Field Monitoring in Smart Agriculture
Keywords:
GeoAI; cooperative robotics; precision agriculture; UAV–UGV systems; smart farming; precision spraying; field monitoring; multi-agent systems; sustainability; agricultural policyAbstract
The convergence of geospatial artificial intelligence (GeoAI) and heterogeneous robotic platforms is reshaping the landscape of precision agriculture by enabling synchronized aerial and ground operations. This paper presents a comprehensive systems-level examination of GeoAI-driven cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) architectures designed for integrated precision spraying and field monitoring. We argue that the synergistic coupling of aerial coverage and ground-level intervention, mediated by real-time GeoAI analytics, offers transformative potential for reducing chemical usage, enhancing crop health surveillance, and improving operational efficiency in large-scale agricultural settings. The study systematically analyzes structural trade-offs in multi-agent coordination, including latency constraints, communication bandwidth limitations, energy budgeting, and sensor fusion dependencies. Particular attention is devoted to the governance of decentralized decision-making algorithms that balance local autonomy with global mission objectives, as well as the infrastructural requirements for edge-cloud computing in rural environments. Sustainability considerations are examined through the lens of lifecycle energy consumption, soil compaction avoidance, and reduction of agrochemical runoff. Policy implications are discussed with respect to airspace regulation, data sovereignty, and equitable access to advanced agricultural technologies. Through a series of conceptual case illustrations and cross-domain comparisons with analogous systems in environmental monitoring and disaster response, the paper identifies critical robustness challenges, such as GPS-denied navigation, adverse weather adaptation, and fault-tolerant reconfiguration. Forward-looking perspectives highlight the integration of foundation models for semantic scene understanding and the potential for autonomous negotiation among heterogeneous fleet agents. The findings underscore that the successful deployment of GeoAI-driven UAV–UGV systems requires not only technological innovation but also careful institutional design and adaptive governance frameworks.
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