article · Knowledge-Based Engineering and Sciences
Accurate prediction of soil temperature is important for agricultural operations and ecological modelling. To assess predictive performance, multiple machine learning methods were compared using hourly hydrometeorological data, including humidity, dew point, rainfall, solar radiation, and barometric pressure. The evaluated methods comprised Random Forest, Support Vector Machine, Neural Network, Linear Regression, and Long Short-Term Memory networks. Among these alternatives, the Long Short-Term Memory architecture delivered the strongest performance, reaching the lowest root mean square error and decreasing the average prediction error by 6 percent compared to Neural Network, Support Vector Machine, and Random Forest models. It also improved prediction accuracy by 15 percent over Linear Regression. The resulting configuration conforms to current machine learning industry standards and functions efficiently on low-powered edge computing equipment.
Soil temperature plays an essential role in plant growth and wider environmental management. Using readily available weather data alongside efficient machine learning methods allows for more accurate local forecasts of ground conditions. Because the leading model operates on low-power hardware, these predictions can be carried out reliably without requiring expensive, high-capacity computing infrastructure.
This research is at an applied and tested stage, demonstrating algorithms that can run on low-powered edge devices. It could enable precision agriculture software developers and environmental monitoring organisations to deploy low-cost, on-site soil temperature forecasting tools. Further operational testing would be required to integrate the model into commercial farm management systems or field-based sensor hardware.
AI-generated from the published abstract. Always read the original work before citing.
The importance of soil temperature (ST) quantification can contribute to diverse ecological modelling processes as well as for agricultural activities. Over the literature, it was evident that soil supports more than 95% of living habitats and food production on earth, and this demand will increase to 500 years’ times in expected consumption in 2060. This paper aims to analyses the contrastive approach to predict the ST of a certain region with the help of different machine learning models, including Random Forest (RF), Support Vector, Neural Network (NN), Linear Regression (LR) and Long Short-Term Memory Network (LSTM). The study was utilized the hourly humidity, dew point, rainfall, solar radiation, and barometer readings for the formulation of the models. Various performance criteria were employed to evaluate the prediction skills of the models and the results depicted that the promising ability belong to LSTM despite the acceptable prediction accuracy achieved by other models. The modelling outcomes revealed that LSTM model attained the lowest root mean square error (RMSE = 3.3255) decreased the average prediction error by 6% with regards to NN (RMSE = 3.4796), SVM (RMSE = 3.5766), and RF (RMSE = 3.8128), and improved the prediction accuracy of LR by 15%. The model is in compliance with the latest machine learning industry standards and allows low-cost experimental performances on low powered edge computing devices.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.51526/kbes.2023.4.1.55-77
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
