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Image Generationr/roboticsPublished: Aug 30, 2026, 04:01 JST1 min read

ESP32-P4 Reads Water Meter with AI

ESP32-P4 Reads Water Meter with AI

Key takeaway

  • An embedded system using the ESP32-P4 now reads water meters. It was trained on real water meter images.

  • Tests confirmed it correctly identified the meter reading.

  • This advances practical on-device AI.

3 Key Points

  1. What happened

    A hobbyist built a system that reads a water meter using the Makerfabs ESP32-P4 board with autofocus camera. They trained a dedicated model on real water meter images, focusing on the pointer and digit-reading area. After deployment, it correctly recognized the meter reading.

  2. Why it matters

    This moves from a proof-of-concept 5-digit recognition demo to a practical application. It shows that AI-based image recognition can run directly on an embedded device for real-world tasks like utility metering.

  3. What to watch

    The system's accuracy on more varied meter conditions, such as different lighting or wear, is not yet reported. Further real-world testing would show if it can be deployed widely.

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Context & Analysis

This project builds on an earlier proof of concept where a 5-digit recognition system ran on the ESP32-S3. The new work steps up to a practical utility application, directly reading a water meter's pointer and digits. Using the Makerfabs ESP32-P4 board with autofocus, the developer gathered new images from an actual meter and trained a dedicated model. The successful recognition of the reading demonstrates that such tasks are possible on embedded hardware without cloud processing. While the results are promising, the article does not detail accuracy metrics or performance under varied conditions, so the robustness of the system remains untested. For business readers, this hints at the potential for low-cost, on-device AI in industrial and utility settings, though broader deployment would require further validation.

FAQ

What board and camera were used?
The Makerfabs ESP32-P4 board with an autofocus camera was used.
How was the model trained?
New images were collected from a real water meter, focusing on the pointer and digit-reading area. A dedicated recognition model was trained on this dataset.
Did the system work on the real water meter?
Yes, after training and deployment, it was tested with real water meter images and correctly recognized the meter reading.

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