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Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine

Matteo Bertocco1, Stefano Parrino2, Giacomo Peruzzi1

  • 1Department of Information Engineering, University of Padova, 35131 Padova, Italy.

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Summary

This study introduces an augmented sensing method using machine learning to accurately estimate soil volumetric water content (VWC) for underground IoT applications. The combined sensor and signal strength approach significantly improves VWC estimation accuracy.

Keywords:
IoTIoUTLoRaLoRaWANaugmented sensormachine learningprecision agriculturesoil moisture sensorvirtual sensor

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Area of Science:

  • Agricultural Engineering
  • Internet of Things (IoT)
  • Sensor Networks

Background:

  • Accurate soil volumetric water content (VWC) estimation is crucial for various applications, including precision agriculture and environmental monitoring.
  • Existing soil moisture sensors often face limitations in accuracy, cost, or deployment range.
  • The Internet of Underground Things (IoUT) presents unique challenges for sensor data transmission and interpretation.

Purpose of the Study:

  • To propose and evaluate an augmented sensing method for improved VWC estimation in soil.
  • To leverage machine learning algorithms to combine data from low-cost soil moisture sensors and LoRaWAN Received Signal Strength Indicator (RSSI).
  • To demonstrate the effectiveness of this augmented approach for IoUT applications.

Main Methods:

  • An Internet of Underground Things (IoUT) sensor node equipped with a low-cost soil moisture sensor and a LoRaWAN transceiver was developed.
  • Machine learning models were trained using a dataset comprising soil moisture sensor readings and LoRaWAN RSSI values.
  • Three VWC estimation techniques were compared: calibrated sensor, virtual sensor (RSSI only), and augmented sensor (combined data).

Main Results:

  • The calibrated soil moisture sensor achieved a Root Mean Square Error (RMSE) of 3.33%.
  • A virtual sensor utilizing only LoRaWAN RSSI yielded an RMSE of 8.67%.
  • The augmented sensing method, combining sensor readings and RSSI, achieved an RMSE of 1.84%, further improving to 1.53% with post-processing.

Conclusions:

  • The augmented sensing method significantly outperforms traditional calibrated sensors and virtual sensors for VWC estimation.
  • Combining low-cost sensor data with LoRaWAN RSSI offers a robust and accurate solution for VWC monitoring in IoUT systems.
  • This approach enhances the reliability and precision of soil moisture measurements, paving the way for more efficient resource management.