Spatiotemporal Fertilizer Ballistics: A Serverless Edge-Computing Architecture for Variable Rate Application in Agricultural Aviation

The precision delivery of bulk solid fertilizers via agricultural aviation is critically constrained by the latency of localized avionics hardware. Traditional material flow models struggle to dynamically adjust release trajectories in response to real-time micro-climatic shifts, high-velocity wind shear, and topological variances, frequently resulting in severe agricultural waste runoff and environmental degradation. This paper proposes a cloud-native, edge-orchestrated architectural framework utilizing Amazon Web Services to construct a real-time machine learning pipeline for precision agriculture. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms and spatiotemporal kinematics, the proposed system programmatically ingests high-frequency telemetry from aircraft sensors. The system dynamically calculates algorithmic fertilizer ballistics, instantly adjusting variable rate application gateways at the network edge. Preliminary architectural evaluations demonstrate that decoupling the ballistic inference engine from legacy onboard hardware significantly reduces actuation latency, providing agronomists and aviation engineers with a deterministic, highly scalable technological foundation for optimizing aerial topdressing and mitigating environmental impact.