Edge-Cloud Collaborative Large Vision Models for Real-Time Agricultural Monitoring and Decision Support
Keywords:
edge-cloud computing; large vision models; agricultural monitoring; decision support; data governance; rural digital infrastructureAbstract
Recent advances in large vision models have created new opportunities for agricultural monitoring, but their computational intensity, energy requirements, and reliance on stable connectivity conflict with rural operational realities. Large vision models require substantial computation and data movement, and naive cloud-only or edge-only deployments often fail to satisfy latency, privacy, and resilience needs. This paper develops a system-level analysis of edge-cloud collaborative architectures that distribute large vision model workloads across field devices, local gateways, regional edge nodes, and centralized cloud resources to support real-time agricultural decision making. We examine structural trade-offs among inference latency, model accuracy, data sovereignty, model update frequency, infrastructure resilience, and environmental sustainability. The paper argues that agricultural AI systems must be understood not only as technical pipelines but also as socio-technical infrastructures embedded in heterogeneous farm economies, rural labor markets, and environmental governance regimes. Drawing on edge intelligence, model compression, federated learning, and agricultural informatics literatures, we identify design principles for adaptive systems that can operate under intermittent connectivity, constrained energy budgets, and diverse stakeholder needs. We further discuss policy implications related to data ownership, algorithmic fairness, and rural digital equity. The analysis highlights the importance of participatory design and regional coordination in building robust, accountable, and sustainable agricultural monitoring systems. The paper concludes with future research directions in edge-native foundation models, multi-modal crop analytics, and governance frameworks for agricultural automation.
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