GeoAI and Edge AI for Smart Infrastructure
The challenge
Pond managers require automated, intelligent decisions—not just simple alerts—for critical functions like aeration, early disease detection, and neighbor-aware actions that factor in spatial correlations across multi-pond clusters. This necessitates an advanced solution where automated controls and predictive analytics ensure optimal yield and resource management.
What we built
EurthTech designed an on-site intelligent hub that is a hybrid Edge/Cloud system. The local base unit combines multi-sensor telemetry (DO, pH, temp) with Edge AI embedded systems for closed-loop control. This design enables the system to autonomously modulate aerators and pumps within seconds, while the cloud layer provides advanced GeoAI for cross-pond forecasting and retraining, supporting a precise Predictive maintenance AI IoT strategy.
How it works
The embedded systems development includes the Edge module running two classes of models: (a) closed-loop controllers (quantised MLPs) for immediate pump/aerator modulation, and (b) anomaly detectors (TinyML models) that detect sudden DO collapse or algal bloom precursors. This local inference capability, driven by our expertise and utilizing LiteRT/Edge Impulse runtimes, ensures rapid, safe operation. Cloud models leverage AI GIS analytics to create a predictive twin for coordinated decision-making (e.g., cross-pond balancing and spare-part allocation).
Results
Properly configured pilots demonstrated significant impact, with control models estimating greater than 30% energy savings potential by scheduling aeration bursts only when needed. The GeoAI twin supports optimized decision-making and resource coordination across clusters.
Scalability & GIS Context: The solution leverages LoRa/Cellular backhaul and incorporates Blockchain for integrity of critical records. The cross-pond coordination and spatial modeling confirm our core strength as a GIS consulting company capable of delivering integrated geospatial engineering services for large-scale agricultural and municipal management.