Agri-UAV-Inference: A High-Throughput Distributed System for Precision Agriculture via Financial-Grade Time Series Forecasting and Edge-AI Swarm Intelligence
Keywords:
Precision Agriculture, Swarm Intelligence, Edge AI, Time Series Forecasting, Distributed Systems, UAV Infrastructure, Socio-Technical SystemsAbstract
The global agricultural sector is undergoing a fundamental transformation characterized by the integration of autonomous systems and high-frequency data analytics. Precision agriculture, once limited to static soil sampling and periodic satellite imagery, now demands real-time, high-throughput intelligence to manage the complexities of climate volatility and resource scarcity. This paper proposes Agri-UAV-Inference, a novel distributed system-level infrastructure that leverages Unmanned Aerial Vehicle (UAV) swarms and edge-based artificial intelligence to perform precision agricultural tasks. We introduce a cross-domain architectural paradigm that applies financial-grade time series forecasting techniques—originally developed for high-frequency trading—to the biological and environmental datasets of modern farming. By treating crop health, soil moisture, and pest migration as high-dimensional financial-like signals, the system achieves a level of predictive granularity previously unattainable. The proposed framework emphasizes hardware-aware distributed inference, where the computational load is dynamically partitioned between the UAV swarm at the edge and a regional cloud backbone. Our analysis explores the structural trade-offs between swarm communication latency, predictive accuracy, and energy sustainability. Furthermore, we examine the socio-technical dimensions of this infrastructure, including the governance of autonomous swarms, the ethics of data sovereignty in rural communities, and the policy implications for global food security. By synthesizing swarm intelligence with robust financial-grade forecasting pipelines, Agri-UAV-Inference provides a scalable blueprint for resilient, high-throughput agricultural systems. The research concludes with a forward-looking perspective on the regulatory challenges of autonomous aerial AI and the role of distributed systems in achieving long-term agricultural sustainability.
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