# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 5 Conclusions and Limitations  
The accuracy of the prediction of environmental variables, such as soil temperature, can vary considerably depending on the modeling approach used. An accurate prediction of the soil temperature is crucial for agricultural productivity, as it directly affects crop growth and development. By using reliable forecasting methods, farmers can optimize key agricultural operations, such as planting schedules, irrigation timing, and fertilizer application, making resource use more efficient and maximizing crop yields. As a result, predicting soil temperature is a fundamental part of precision agriculture and an essential element of modern farming practices. In this study, the ANFIS demonstrated particularly strong performance, achieving low error rates and high prediction accuracy. Other machine learning algorithms, including RF, XGB, KNN, SVR, and MLP, also delivered robust results. By contrast, the MFIS showed limitations in capturing nonlinear relationships in the data. The MLR model provided a performance comparable to some machine learning methods. This can be explained by the statistical results: when the soil depth and the average air temperature were considered as independent variables, the average air temperature had a positive and statistically significant effect on the soil temperature, while the effect of depth alone was not significant (\(p\) = 0.423). The introduction of an interaction term between depth and average temperature as an additional predictor improved the model, with all coefficients reaching statistical significance, and the coefficient of determination approaching that of the machine learning models. The choice of modeling technique should be based on the specific application requirements, data availability, and system complexity. Predicting the soil temperature at different depths using accessible data, such as soil depth and air temperature, offers considerable practical benefits for agricultural management. Predictions derived from fuzzy logic, machine learning, and regression-based models can inform and optimize key field activities, such as planting, irrigation, fertilization, tillage, and drainage. Ultimately, these tools provide valuable support to farmers and agricultural practitioners by facilitating the development of smart farming systems that improve decision-making and operational efficiency. Nevertheless, some limitations of this study must be acknowledged. The analysis was limited to only two input variables--air temperature and soil depth--which, while practical, may limit the ability of the models to fully capture the complex atmospheric interactions that influence soil temperature dynamics. In addition, this study was conducted in a specific climatic and geographic context, which may limit the generalizability of the results. Future research should aim to evaluate the performance of the model incorporating weather forecasts and a wider range of meteorological variables in different climatic regions. Such studies would help to improve the robustness, adaptability, and broader applicability of soil temperature prediction models in different agro-ecological zones. Conceptualization, B.C., Y.K. and E.C. ; methodology, B.C., Y.K., E.C., E.K. and H.S. ; software, B.C. and E.K. ; validation, B.C., E.C. and H.S. ; formal analysis, B.C., E.C. and E.K. ; investigation, B.C., Y.K. and E.C. ; resources, B.C., Y.K. and E.C. ; data curation, B.C., E.C. and H.S. ; writing--original draft preparation, B.C., Y.K. and E.C. ; writing--review and editing, B.C., E.K. and H.S. ; visualization, B.C., Y.K. and E.K. ; supervision, B.C. and H.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. **Acknowledgments:** The authors have reviewed and edited the output and take full responsibility for the content of this publication. **Conflicts of Interest:** The authors declare no conflicts of interest.