Traditional irrigation systems depend heavily on manual inspection to detect leaks, pipe blockages, and abnormal water flow, resulting in significant water wastage, reduced irrigation efficiency, and increased maintenance expenditure. This paper presents AquaVision BI, an intelligent IoT-enabled irrigation monitoring system that integrates three Hall-effect flow-rate sensors, an ESP32 Wi-Fi microcontroller, and an AI-driven differential-threshold anomaly-detection algorithm to achieve real-time surveillance of irrigation pipelines. The system continuously samples sensor pulse counts at one-second intervals, computes volumetric flow rates, and applies pairwise differential analysis to localise leakage to specific pipeline segments (upstream, mid-stream, or downstream). Upon anomaly detection, automated alerts are dispatched via the Blynk IoT cloud dashboard and a local buzzer actuator. Experimental evaluation on a controlled testbed confirms accurate leak localisation across all three sensor nodes, with end-to-end alert latency consistently below two seconds. The proposed system significantly reduces water wastage, lowers operational costs, and promotes sustainable agricultural water management practices.
Patil et al. (Thu,) studied this question.
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