Soil Temperature Prediction via Self-Training: Izmir Case

Göksu TÜYSÜZOĞLU; Derya BİRANT; Volkan KIRANOGLU

doi:https://doi.org/10.15832/ankutbd.775847

Makale detayları

Soil Temperature Prediction via Self-Training: Izmir Case

Yazarlar:

Göksu TÜYSÜZOĞLU | Derya BİRANT | Volkan KIRANOGLU

Yayıncı:

Ankara University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Journal of Agricultural Sciences 2022 Vol.28 Issue 1, pp.47-62

Belge Tipi:

Makale

Yayın Tarihi:

2/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000606703

Sıklık:

Üç Aylık

eISSN:

2148-9297

DOI:

https://doi.org/10.15832/ankutbd.775847

Özet:

 

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.

Makale detayları

Soil Temperature Prediction via Self-Training: Izmir Case

Yazarlar: Göksu TÜYSÜZOĞLU | Derya BİRANT | Volkan KIRANOGLU

Yayıncı: Ankara University

Ana Başlıklar: Uygulamalı fizik

Dergi: Journal of Agricultural Sciences 2022 Vol.28 Issue 1, pp.47-62

Belge Tipi: Makale

Yayın Tarihi: 2/2022

Belge Dili: İngilizce

Document ID (Org No.): 2000606703

Sıklık: Üç Aylık

eISSN: 2148-9297

DOI: https://doi.org/10.15832/ankutbd.775847

Sayı

Yazarlar:

Göksu TÜYSÜZOĞLU | Derya BİRANT | Volkan KIRANOGLU

Yayıncı:

Ankara University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Journal of Agricultural Sciences 2022 Vol.28 Issue 1, pp.47-62

Belge Tipi:

Makale

Yayın Tarihi:

2/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000606703

Sıklık:

Üç Aylık

eISSN:

2148-9297

DOI:

https://doi.org/10.15832/ankutbd.775847