“Super-Sensory” Environmental Monitoring System Based on UNIHIKER M10
This article presents a “super-sensory” environmental monitoring system built on the DFRobot FireBeetle 2 ESP32-S3-U microcontroller and the UNIHIKER M10 Python educational controller. By integrating multiple sensors—including a soil moisture sensor, a five-in-one environmental sensor, and an air quality sensor—the system achieves real-time, high-precision sensing of environmental parameters. The FireBeetle 2 ESP32-S3-U serves as an efficient sensor node responsible for data acquisition and fusion, while the UNIHIKER M10 acts as the intelligent interaction hub, offering intuitive visualization and smart alerting capabilities. The article explores the system’s hardware composition, software architecture, operating principles, build process, and real-world performance, providing AIoT enthusiasts and developers with a powerful yet accessible environmental monitoring solution to help safeguard environmental health.
Hardware Overview: The Foundation of the “Super-Sensory” System
A system capable of precise environmental perception relies on sensitive sensors and powerful controllers. This section introduces the core hardware components, each fulfilling a specific role and working together to deliver robust data acquisition and processing capabilities.
Sensor Suite: Sensitive Probes into the Environment
Sensors function as the “eyes” and “nose” of the system, converting physical or chemical signals into electrical data for processing. This project integrates three key sensors, covering soil moisture, comprehensive environmental parameters, and air quality:
Gravity: Capacitive Analog Waterproof Soil Moisture Sensor (SEN0308)
Features and characteristics: This sensor acts as a “personal doctor” for plants. Using capacitive sensing, it accurately measures soil moisture without direct electrical contact, avoiding corrosion issues common in resistive sensors and significantly extending lifespan. Its waterproof design enables long-term insertion into soil, providing continuous feedback on plant hydration and enabling data-driven irrigation.
Role in the project: In the smart planter scenario, it continuously monitors soil moisture. When levels fall below a preset threshold, the system issues watering reminders, ensuring optimal plant growth conditions.
Gravity: Five-in-One Environmental Sensor (SEN0501)
Features and characteristics: This highly integrated sensor measures temperature, humidity, barometric pressure, ambient light, and UV intensity simultaneously. By consolidating multiple measurements into one module, it simplifies wiring and data acquisition, making it ideal for comprehensive environmental monitoring systems.
Role in the project: It provides a macro-level view of indoor or outdoor environments. For example, temperature and humidity data can drive HVAC control, light intensity can inform lighting decisions, and UV levels can prompt sun-protection alerts. These inputs are critical for intelligent analysis and decision-making.
Gravity: I2C SGP40 Air Quality Sensor (SEN0394)
Features and characteristics: Indoor air quality directly impacts health, yet harmful VOCs are invisible. The SGP40 sensor sensitively detects VOC concentration, providing an indirect measure of air quality. With an I2C interface, it offers stable, reliable communication and easy integration.
Role in the project: As a core alert sensor, it triggers warnings when VOC levels rise. The UNIHIKER M10 then presents visual alerts and voice prompts, advising ventilation to mitigate health risks.
Controller Suite: The Core of Data Processing and Intelligent Interaction
If sensors are the system’s sensory organs, controllers are its brain and nervous system. This project employs both the FireBeetle 2 ESP32-S3-U and the UNIHIKER M10, working together to deliver powerful functionality.
FireBeetle 2 Board ESP32-S3-U (DFR0975-U)
Core role: This board serves as the system’s central processing unit, handling multi-sensor data acquisition, fusion, and preliminary analysis.
AI and edge computing: The ESP32-S3 features strong AI capabilities and supports edge inference, enabling local data analysis without constant cloud connectivity. This reduces latency, enhances privacy, and opens future possibilities such as image-based plant disease detection or water quality analysis.
Data processing: In this project, it gathers raw data from soil moisture, environmental, and air quality sensors, performs initial processing, and transmits packaged data to the UNIHIKER M10 via Wi-Fi.
UNIHIKER M10 Python Educational Controller (DFR0706-EN)
Interactive hub: The UNIHIKER M10 provides a clear, visual interface for real-time data display, using charts and dashboards to convey environmental status at a glance.
Smart alerts and audio feedback: Combined with the Fermion DF1201S DFPlayer PRO MP3 module and a speaker, it delivers audible alerts during abnormal conditions.
Python-friendly development: Its built-in Python environment and extensive libraries lower the barrier to AIoT development, enabling efficient data handling, visualization, and logic control.
Together, the FireBeetle 2 ESP32-S3-U handles low-level data acquisition and processing, while the UNIHIKER M10 focuses on presentation, advanced analysis, and user interaction—forming a complementary and powerful system architecture.
Software Architecture: The Intelligence Behind the System
While hardware provides structure, software delivers intelligence. In this system, software manages data acquisition, transmission, processing, visualization, and alerts.
MQTT Protocol: The Data Bridge
MQTT (Message Queuing Telemetry Transport) is used for communication between the FireBeetle 2 ESP32-S3-U and the UNIHIKER M10. As a lightweight publish/subscribe protocol optimized for constrained devices and unreliable networks, it is ideal for IoT applications.
Publish/subscribe model: Publishers send messages to topics, while subscribers receive messages from topics of interest via a broker. This decoupling improves flexibility and scalability.
Efficiency: With minimal overhead and support for multiple QoS levels, MQTT ensures reliable and efficient data transfer.
Project implementation:
The FireBeetle 2 ESP32-S3-U publishes JSON-formatted sensor data to topics such as sensor/soil_moisture, sensor/environment, and sensor/air_quality.
The UNIHIKER M10 subscribes to these topics, receiving and parsing data in real time. It can function as an MQTT client or even as a broker, simplifying deployment.
Data Processing and Visualization
Upon receiving data, the UNIHIKER M10 parses JSON payloads, stores recent values in buffers, performs basic fusion or calculations, and visualizes results through real-time plots and dashboards. Time-series curves show trends, while gauges and color-coded indicators provide intuitive status feedback, ensuring a user-friendly experience.
Intelligent Alerts and Voice Feedback
Threshold-based logic triggers alerts when parameters exceed safe limits. Visual cues on the screen are complemented by audio warnings via the DFPlayer PRO module and speaker. For example, rising VOC levels prompt a spoken alert advising ventilation, ensuring timely response even when users are not watching the display.
System Assembly and Deployment
Thanks to DFRobot’s modular hardware design, assembly is straightforward:
-Baseplate design and 3D printing for stable mounting
-Header soldering on the FireBeetle 2 ESP32-S3-U
-Secure mounting of sensors and controllers
-Neat wiring and cable management for reliability
Software Deployment
The FireBeetle 2 ESP32-S3-U handles sensor data acquisition and MQTT publishing, with network credentials isolated in a secrets.h file for maintainability. The UNIHIKER M10 runs Python-based MQTT clients, visualization routines, alert logic, and audio playback. Code is uploaded via SSH, simplifying development for beginners.
Real-World Performance: An Immersive Smart Experience
In practice, the system delivers accurate, responsive monitoring. Soil moisture trends visualize water absorption dynamics, while air quality alerts respond instantly to VOC spikes during cooking or cleaning. The combination of precise sensing, intuitive visualization, and audio feedback transforms raw data into actionable insights.
Conclusion and Outlook
This “super-sensory” environmental monitoring system demonstrates the power and accessibility of DFRobot’s AIoT ecosystem. By integrating Gravity sensors, the FireBeetle 2 ESP32-S3-U, and the UNIHIKER M10, it achieves real-time environmental awareness, intelligent analysis, and multimodal alerts.
Future enhancements may include deeper AI integration with vision modules, cloud-based analytics, automated system linkage, low-power optimization, and personalized user interfaces. As AIoT technology continues to evolve, systems like this will play an increasingly important role in building smarter, healthier, and more sustainable environments.
