Konecny Jiri, Konecny Jaromir, Bancik Kamil, Mikus Miroslav, Choutka Jan, Koziorek Jiri, Hameed Ibrahim A, Valinevicius Algimantas, Andriukaitis Darius, Prauzek Michal
Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic.
Department of Mechanical Engineering and Technology Management, Norwegian University of Life Sciences (NMBU), Drøbakveien 31, 1433 Ås, Norway.
Sensors (Basel). 2025 Jul 7;25(13):4232. doi: 10.3390/s25134232.
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.
专为环境和农业目的设计的物联网（IoT）传感器可为创造可持续的绿色环境做出重大贡献。然而，为这些传感器供电仍然是一个挑战，利用空气和土壤之间的温差似乎是一个很有前景的解决方案。对于能量收集技术，需要准确的土壤温度剖面数据。本研究使用在捷克共和国收集的气象和土壤温度剖面数据，训练基于多项式回归（PR）、支持向量回归（SVR）和长短期记忆（LSTM）的机器学习模型来预测土壤温度剖面。研究结果表明误差为0.79°C，比现有研究报告的温度误差低约10.9%。除了实现更低的温度预测误差外，所提出的解决方案还将模型的输入参数简化为仅环境温度和太阳辐照度。这一改进显著降低了与回归模型相关的计算成本，为预测土壤温度提供了一种更有效的方法，以优化物联网传感器中的能量收集。
