Recent Technological Upgrades to the SHYPROM IoT-Based System for Monitoring Soil Water Status

Effective water resource management plays a crucial role in achieving sustainability in agriculture, hydrology, and environmental protection, particularly under growing water scarcity and climate-related challenges. Soil moisture (θ), matric potential (<i>h</i>), and hydraulic conductivity (<i>K</i>) are critical parameters influencing water availability for crops and regulating hydrological, environmental, and ecological processes. To address the need for accurate, real-time soil monitoring in both laboratory and open-field conditions, we proposed an innovative IoT-based monitoring system called SHYPROM (Soil HYdraulic PROperties Meter), designed for the simultaneous estimation of parameters θ, <i>h</i>, and <i>K</i> at different soil depths. The system integrates capacitive soil moisture and matric potential sensors with wireless communication modules and a cloud-based data processing platform, providing continuous, high-resolution measurements. SHYPROM is intended for use in both environmental and agricultural contexts, where it can support precision irrigation management, optimize water resource allocation, and contribute to hydrological and environmental monitoring. This study presents recent technological upgrades to the proposed monitoring system. To improve the accuracy and robustness of θ estimates, the capacitive module was enhanced with an integrated oscillator circuit operating at 60 MHz, an upgrade from the previous version, which operated at 600 kHz. The new system was tested (i.e., calibrated and validated) through a series of laboratory experiments on soils with varying textures, demonstrating its improved ability to capture dynamic soil moisture changes with greater accuracy compared to the earlier SHYPROM version. During calibration and validation tests, soil water content data were collected across a θ range from 0 to 0.40 cm<sup>3</sup>/cm<sup>3</sup>. These measurements were compared to reference θ values obtained using the thermo-gravimetric method. The results show that the proposed monitoring system can be used to obtain predictions of θ values with acceptable accuracy (<i>R</i><sup>2</sup> values range between 0.91 and 0.96). To further validate the performance of the upgraded SHYPROM system, evaporation experiments were also conducted, and the θ(<i>h</i>) and <i>K</i>(θ) relationships were determined among soils. Retention and conductivity data were fitted using the van Genuchten and van Genuchten-Mualem models, respectively, confirming that the device accurately captures the temporal evolution of soil water status (<i>R</i><sup>2</sup> values range from 0.97 to 0.99).

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Publication Details

Journal
Sensors
Published
2025-08-09
DOI
https://doi.org/10.3390/s25164934
Citations
2
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
1.60
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article

Recent Technological Upgrades to the SHYPROM IoT-Based System for Monitoring Soil Water Status

Alessandro Comegna, Shawkat Basel Mostafa Hassan, Antonio Coppola
2 citations
Sensors
Soil Moisture and Remote Sensing
1.60
article

Recent Technological Upgrades to the SHYPROM IoT-Based System for Monitoring Soil Water Status

Alessandro Comegna, Shawkat Basel Mostafa Hassan, Antonio Coppola
article en
2 citations

Abstract

Effective water resource management plays a crucial role in achieving sustainability in agriculture, hydrology, and environmental protection, particularly under growing water scarcity and climate-related challenges. Soil moisture (θ), matric potential (<i>h</i>), and hydraulic conductivity (<i>K</i>) are critical parameters influencing water availability for crops and regulating hydrological, environmental, and ecological processes. To address the need for accurate, real-time soil monitoring in both laboratory and open-field conditions, we proposed an innovative IoT-based monitoring system called SHYPROM (Soil HYdraulic PROperties Meter), designed for the simultaneous estimation of parameters θ, <i>h</i>, and <i>K</i> at different soil depths. The system integrates capacitive soil moisture and matric potential sensors with wireless communication modules and a cloud-based data processing platform, providing continuous, high-resolution measurements. SHYPROM is intended for use in both environmental and agricultural contexts, where it can support precision irrigation management, optimize water resource allocation, and contribute to hydrological and environmental monitoring. This study presents recent technological upgrades to the proposed monitoring system. To improve the accuracy and robustness of θ estimates, the capacitive module was enhanced with an integrated oscillator circuit operating at 60 MHz, an upgrade from the previous version, which operated at 600 kHz. The new system was tested (i.e., calibrated and validated) through a series of laboratory experiments on soils with varying textures, demonstrating its improved ability to capture dynamic soil moisture changes with greater accuracy compared to the earlier SHYPROM version. During calibration and validation tests, soil water content data were collected across a θ range from 0 to 0.40 cm<sup>3</sup>/cm<sup>3</sup>. These measurements were compared to reference θ values obtained using the thermo-gravimetric method. The results show that the proposed monitoring system can be used to obtain predictions of θ values with acceptable accuracy (<i>R</i><sup>2</sup> values range between 0.91 and 0.96). To further validate the performance of the upgraded SHYPROM system, evaporation experiments were also conducted, and the θ(<i>h</i>) and <i>K</i>(θ) relationships were determined among soils. Retention and conductivity data were fitted using the van Genuchten and van Genuchten-Mualem models, respectively, confirming that the device accurately captures the temporal evolution of soil water status (<i>R</i><sup>2</sup> values range from 0.97 to 0.99).

SensorsVol. 25(16)
University of Cagliari (IT), University of Basilicata (IT)
Zero hunger
Openalex Percentile: Top 16%
Soil Moisture and Remote Sensing
1.60
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