Abstract
This paper proposes a new model, called Soil Temperature prediction via Self-Training (STST), which successfully estimates the soil temperature at various soil depths by using machine learning methods. The previous studies on soil temperature prediction only use labeled data which is composed of a variable set X and the corresponding target value Y. Unlike the previous studies, our proposed STST method aims to raise the sample size with unlabeled data when the amount of pre-labeled data is scarce to form a model for prediction. In this study, the hourly soil-related data collected by IoT devices (Arduino Mega, Arduino Shield) and some sensors (DS18B20 soil temperature sensor and soil moisture sensor) and meteorological data collected for nearly nine months were taken into consideration for soil temperature estimation for future samples. According to the experimental results, the proposed STST model accurately predicted the values of soil temperature for test cases at the depths of 10, 20 30, 40, and 50 cm. The data was collected for a single soil type under different environmental conditions so that it contains different air temperature, humidity, dew point, pressure, wind speed, wind direction, and ultraviolet index values. Especially, the XGBoost method combined with self-training (ST-XGBoost) obtained the best results at all soil depths (R2 0.905-0.986, MSE 0.385-2.888, and MAPE 3.109%-8.740%). With this study, by detecting how the soil temperature will change in the future, necessary precautions for plant development can be taken earlier and agricultural returns can be obtained beforehand.
Highlights
This paper proposes a new model, called Soil Temperature prediction via Self-Training (STST), which successfully estimates the soil temperature at various soil depths by using machine learning methods
Because of the importance of the subject, various different regression and statistical analysis techniques were proposed considering machine learning such as support vector machines (SVM) (Xing et al 2018), to estimate soil temperature, and decision tree regression (Pekel 2020) and the least-squares support vector machine (Ren et al 2019) to predict soil moisture and Tuysuzoglu et al - Journal of Agricultural Sciences (Tarim Bilimleri Dergisi), 2022, 28(1): 47-62 collaborative data mining using the algorithms of local polynomial regression, neural networks, k-nearest neighbor, support vector machine (Anton et al 2019) to estimate both soil temperature and soil moisture
Three performance metrics (mean squared error (MSE) given in Equation 8, coefficient of determination (R2) given in Equation 9, and mean absolute percentage error (MAPE) given in Equation 10, where ST is the measured value of soil temperature, ST is the predicted value of soil temperature, ̅STis the mean of the observed data, and n is the number of samples) were calculated to evaluate the usability of the proposed methodology and to select the best one in terms of the given criteria
Summary
This paper proposes a new model, called Soil Temperature prediction via Self-Training (STST), which successfully estimates the soil temperature at various soil depths by using machine learning methods. Soil temperature plays an important role in agriculture since it is closely related to the myriad events occurring in the soil It is a very important ecological factor that affects plant life at all stages from seed germination to seedling growth and development. Soil resistance to the physical events such as erosion and subsidence can drop dramatically at high soil temperatures For this reason, factors affecting soil warming and control of soil temperature are extremely important. Because of the importance of the subject, various different regression and statistical analysis techniques were proposed considering machine learning such as support vector machines (SVM) (Xing et al 2018), to estimate soil temperature, and decision tree regression (Pekel 2020) and the least-squares support vector machine (Ren et al 2019) to predict soil moisture and Tuysuzoglu et al - Journal of Agricultural Sciences (Tarim Bilimleri Dergisi), 2022, 28(1): 47-62 collaborative data mining using the algorithms of local polynomial regression, neural networks, k-nearest neighbor, support vector machine (Anton et al 2019) to estimate both soil temperature and soil moisture
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