Implementing AI on low-power embedded devices for digital water meter identification and data transfer via lora network

Abstract

This study introduces an artificial intelligence system implemented on the ESP32-CAM platform, aimed at conducting optical character recognition (OCR) on water meters. Leveraging LoRa technology for data transmission ensures efficient energy utilization and convenient long-range communication capabilities. The system achieves an impressive accuracy rate of 98.2% in identifying water meter readings, showcasing its reliability. Proposed as a feasible solution, it offers the advantages of low energy consumption, cost-effectiveness, and flexibility in widespread deployment, particularly leveraging existing water meter infrastructure. Thus, this research presents a promising choice for various applications beyond merely reading water meter readings. Its efficient and accurate OCR functionality makes it suitable for diverse scenarios, ranging from smart city initiatives to industrial automation processes. With its ability to integrate seamlessly into existing infrastructure and deliver reliable results, this system stands poised to revolutionize OCR applications in various domains, contributing to enhanced efficiency and productivity.

Keywords

• lora
• machine learning
• water meter
• image processing
• low power