Gaia Edge is an end-to-end AI precision farming platform engineered for real-time agricultural intelligence at the edge. Across eight focused sprints, we built a vertically integrated system that spans crop yield prediction, disease detection, soil health monitoring, harvest intelligence, and weather-driven risk modelling. Each capability is designed not as an isolated demo model, but as a connected decision pipeline — turning raw sensor, image, and weather data into actionable agronomic insights.
The platform runs natively on NVIDIA GB10, leveraging GPU acceleration and TensorRT optimisation to deliver high-performance edge inference. From YOLOv8 crop disease detection to FP16 TensorRT engines achieving ~3× latency reduction, Gaia Edge demonstrates how precision agriculture workloads can be executed efficiently at the edge — with measurable improvements in throughput, energy efficiency per inference, and deterministic performance without thermal throttling. CPU and GPU orchestration is treated as a first-class architectural component, not an afterthought.
Sprint 1–8 culminate in a unified precision agriculture intelligence stack. We combined computer vision, IoT soil telemetry, Open-Meteo weather integration, yield modelling, and harvest maturity prediction into a cohesive pipeline — fully surfaced through an interactive Streamlit interface and visualised via the Nexus pipeline viewer. The result is a production-ready blueprint for AI-driven farming systems: scalable, GPU-accelerated, and purpose-built for intelligent decision-making at the edge.
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