TY  - JOUR
T1  - Soil Temperature Prediction via Self-Training: Izmir Case
AU  - Tüysüzoğlu, Göksu
AU  - Birant, Derya
AU  - Kıranoglu, Volkan
PY  - 2022
DA  - February
Y2  - 2021
DO  - 10.15832/ankutbd.775847
JF  - Journal of Agricultural Sciences
JO  - J Agr Sci-Tarim Bili
PB  - Ankara University
WT  - DergiPark
SN  - 1300-7580
SP  - 47
EP  - 62
VL  - 28
IS  - 1
LA  - en
AB  - 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.
KW  - Soil temperature prediction
KW  - Self-training
KW  - Regression
KW  - Machine learning
KW  - Agriculture
KW  - Artificial Intelligence
KW  - STST
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UR  - https://doi.org/10.15832/ankutbd.775847
L1  - https://dergipark.org.tr/en/download/article-file/1222746
ER  -