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. 2025 Jul 7;25(13):4232.
doi: 10.3390/s25134232.

Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors

Jiri Konecny  1 , Jaromir Konecny  1 , Kamil Bancik  1 , Miroslav Mikus  1 , Jan Choutka  1 , Jiri Koziorek  1 , Ibrahim A Hameed  2 , Algimantas Valinevicius  3 , Darius Andriukaitis  3 , Michal Prauzek  1
Affiliations

Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors

Jiri Konecny et al. Sensors (Basel). .

Abstract

Internet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising solution. For energy-harvesting technologies, accurate soil temperature profile data are needed. This study uses meteorological and soil temperature profile data collected in the Czech Republic to train machine learning models based on Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict the soil temperature profile. The results of the study indicate an error of 0.79 °C, which is approximately 10.9% lower than the temperature error reported in state-of-the-art studies. Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. This improvement significantly reduces the computational costs associated with the regression model, offering a more efficient approach to predicting soil temperature for the purpose of optimizing energy harvesting in IoT sensors.

Keywords: Internet-of-Things sensors; energy harvesting; long short-term memory; polynomial regression; support vector regression; temperature modelling.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Diagram illustrating weather parameters as inputs to a model designed for estimating the soil temperature profile, thereby simulating the behaviour of a thermoelectric generator-powered IoT sensor.
Figure 2
Figure 2
The energy-harvesting device deployed at the experiment site.
Figure 3
Figure 3
LSTM model structure: Main structure, layers, activation function definitions, inputs and outputs.
Figure 4
Figure 4
Diagram of the experimental workflow and specific steps.
Figure 5
Figure 5
Box plot of environmental temperatures and soil temperatures.
Figure 6
Figure 6
(a) Air and soil temperature correlation heat map. (b) Weather conditions and air temperature correlation heat map.
Figure 7
Figure 7
Comparison of feature sets according to the total score obtained for 100 cm soil depth.
Figure 8
Figure 8
Comparison of feature sets according to soil depth for the LSTM model.
Figure 9
Figure 9
Errors and best feature set scores for the models: (a) Comparison of MAE and RMSE; (b) Comparison of Total Score at various depths.
Figure 10
Figure 10
Predicted soil temperature versus real temperature at various depths (LSTM model).
Figure 11
Figure 11
Predicted soil temperature deviation at various depths (LSTM model).
See this image and copyright information in PMC

References

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