iot-anomaly-detection

IoT anomaly detection evaluates real-time sensor telemetry against agronomic thresholds to flag deviations in temperature, humidity, and pH levels. It ingests data via FastAPI endpoints and uses Supabase storage to identify and report these environmental anomalies.

Automated device control triggers actions like activating ventilators or turning off irrigation when extreme temperature or humidity anomalies are detected. This proactive decision-making minimizes plant damage by responding directly to threshold evaluations.

Analyze IoT sensor data files by running the 'analyse-iot-data' command with the '-f' flag followed by your file path. This processes the telemetry data to evaluate agronomic thresholds and reports any detected environmental anomalies.

Gemini AI supports proactive analysis by evaluating IoT sensor data to coordinate automated control actions for smart greenhouse environments. It integrates with FastAPI and Supabase to enable intelligent, proactive decision-making for agronomic systems.

You need Supabase for data storage and FastAPI for HTTP endpoints to monitor agricultural IoT telemetry effectively. These dependencies provide the required real-time data ingestion and secure storage infrastructure for threshold evaluation.