Soil Temperature Prediction via Self-Training: Izmir Case

Göksu TÜYSÜZOĞLU; Derya BİRANT; Volkan KIRANOGLU

doi:10.15832/ankutbd.775847

Soil Temperature Prediction via Self-Training: Izmir Case

Authors : Göksu TÜYSÜZOĞLU, Derya BİRANT, Volkan KIRANOGLU

Pages : 47-62

Doi:10.15832/ankutbd.775847

View : 55 | Download : 15

Publication Date : 2022-02-25

Article Type : Research Paper

Abstract :This paper proposes a new model, called Soil Temperature prediction via Self-Training insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(Arduino Mega, Arduino Shield); and some sensors insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(ST-XGBoost); obtained the best results at all soil depths insert ignore into journalissuearticles values(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.
Keywords : Soil temperature prediction, Self training, Regression, Machine learning, Agriculture, Artificial Intelligence, STST

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