Soil Temperature Prediction via Self-Training: Izmir Case
Yıl: 2022 Cilt: 28 Sayı: 1 Sayfa Aralığı: 47 - 62 Metin Dili: İngilizce DOI: 10.15832/ankutbd.775847 İndeks Tarihi: 29-07-2022
Soil Temperature Prediction via Self-Training: Izmir Case
Öz: 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.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- Abyaneh H Z, Varkeshi M B, Golmohammadi G & Mohammadi K (2016). Soil temperature estimation using an artificial neural network and co-active neuro-fuzzy inference system in two different climates. Arabian Journal of Geosciences 9(5): 377. https://10.1007/s12517-016- 2388-8
- Akengin H & Dölek İ (2019). Physical Geography of Turkey. Pegem Akademi, Ankara (In Turkish)
- Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Shahrabadi A H & Gharabaghi B (2020a). Modelling daily soil temperature by hydrometeorological data at different depths using a novel data-intelligence model: deep echo state network model. Artificial Intelligence Review 54: 2863-2890. https://doi.org/10.1007/s10462-020-09915-5
- Alizamir M, Kisi O, Ahmed A N, Mert C, Fai C M, Kim S, Kim N W & El-Shafie A (2020b). Advanced machine learning model for better prediction accuracy of soil temperature at different depths. Plos One 15(4): e0231055. https://doi.org/10.1371/journal.pone.0231055
- Anton C A, Matei O & Avram A (2019). Collaborative data mining in agriculture for prediction of soil moisture and temperature. In: Computer Science On-line Conference (CSOC 2019), 24-27 April, Zlin, Czech Republic pp. 141-151. https://doi.org/10.1007/978-3-030-19807-7_15
- Behmanesh J & Mehdizadeh S (2017). Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region. Environmental Earth Sciences 76(2): 76. https://doi.org/10.1007/s12665-017-6395-1
- Belkin M, Niyogi P & Sindhwani V (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7(Nov): 2399-2434
- Bilgili M (2010). Prediction of soil temperature using regression and artificial neural network models. Meteorology and Atmospheric Physics 110(1-2): 59-70. https://doi.org/10.1007/s00703-010-0104-x
- Bilgili M (2011). The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey. Turkish Journal of Agriculture and Forestry 35(1): 83-93. https://10.3906/tar-1001-593
- Bilgili M (2012). Regional assessment of monthly soil temperatures in the Aegean Region of Turkey. Arabian Journal for Science and Engineering 37(3): 765-775. https://doi.org/10.1007/s13369-012-0199-0
- Bilgili M, Sahin B & Sangun L (2013). Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models. Environmental Monitoring and Assessment 185(1): 347-358. https://doi.org/10.1007/s10661-012-2557-5
- Bolca M, Kurucu Y, Dengiz O & Nahry A D H (2011). Terrain characterization for soils survey of Kucuk Menderes plain, South of Izmir, Turkey, using remote sensing and GIS techniques. Zemdirbyste-Agriculture 98: 93-104
- Citakoglu H (2017). Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theoretical and Applied Climatology 130(1-2): 545-556. https://doi.org/10.1007/s00704-016-1914-7
- Dadashzadeh M, Abbaspour-Gilandeh Y, Mesri-Gundoshmian T, Sabzi S, Hernández-Hernández J L, Hernández-Hernández M & Arribas J I (2020). Weed classification for site-specific weed management using an automated stereo computer-vision machine-learning system in rice fields. Plants 9(5): 559. https://doi.org/10.3390/plants9050559
- Dizdar M Y (2003). Turkey's land resources. Retrieved in January, 10, 2021 from https://www.zmo.org.tr/ (In Turkish)
- Friedl M A (2018). Remote sensing of croplands. Comprehensive Remote Sensing 6: 78-95. https://doi.org/10.1016/B978-0-12-409548- 9.10379-3
- Giraddi S, Desai S & Deshpande A (2020). Deep Learning for Agricultural Plant Disease Detection. In: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications (ICDSMLA 2019), 21-22 Nov, Pune, India, pp. 864-871. https://doi.org/10.1007/978-981-15-1420-3_93
- Gönençgil B, Biricik A S, Atalay İ, Aydınözü D, Çoban A & Ertek A (2016). Turkey physical geography. Retrieved in October, 9, 2021 from http://auzefkitap.istanbul.edu.tr/ (In Turkish)
- Guan Y, Shen Y, Mohammadi B & Sadat M A (2020). Estimation of soil temperature based on meteorological parameters by the HYBRID INVASIVE Weed Optimization Algorithm Model. IOP Conference Series: Earth and Environmental Science 428: 012059. https://10.1088/1755-1315/428/1/012059
- Hamrani A, Akbarzadeh A & Madramootoo C A (2020). Machine learning for predicting greenhouse gas emissions from agricultural soils. Science of the Total Environment 741: 140338. https://doi.org/10.1016/j.scitotenv.2020.140338
- Hillel D (2005). Thermal properties and processes. In: D Hillel (Ed.), Encyclopedia of Soils in the Environment, Academic Press, pp. 156-163
- Huang R, Huang J X, Zhang C, Ma H Y, Zhuo W, Chen Y Y, Zhu D H, Wu Q & Mansaray L R (2020). Soil temperature estimation at different depths, using remotely-sensed data. Journal of Integrative Agriculture 19(1): 277-290. https://doi.org/10.1016/S2095-3119(19)62657-2
- Kapur S, Aydın M, Akça E & Reich P (2018). Climate change and soils. In: S Kapur, E Akça & H Günal (Eds.), The Soils of Turkey, Springer, pp. 45-55. https://doi.org/10.1007/978-3-319-64392-2_4
- Kisi O, Tombul M & Kermani M Z (2015). Modeling soil temperatures at different depths by using three different neural computing techniques. Theoretical and Applied Climatology 121(1-2): 377-387. https://doi.org/10.1007/s00704-014-1232-x
- Kisi O, Sanikhani H & Cobaner M (2017). Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques. Theoretical and Applied Climatology 129(3-4): 833-848. https://doi.org/10.1007/s00704-016-1810-1
- Nandy A & Singh P K (2020). Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: evidence from rural eastern India. Journal of Cleaner Production 267: 122106. https://doi.org/10.1016/j.jclepro.2020.122106
- Niedbała G, Kurasiak-Popowska D, Stuper-Szablewska K & Nawracała J (2020). Application of artificial neural networks to analyze the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat grain. Agriculture 10(4): 127. https://doi.org/10.3390/agriculture10040127
- Ozturk M, Salman O & Koc M (2011). Artificial neural network model for estimating the soil temperature. Canadian Journal of Soil Science 91(4): 551-562. https://doi.org/10.4141/cjss10073
- Pekel E (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology 139(3): 1111-1119. https://doi.org/10.1007/s00704-019-03048-8
- Penghui L, Ewees A A, Beyaztas B H, Qi C, Salih S Q, Al-Ansari N, Bhagat S K, Yaseen Z M & Singh V P (2020). Metaheuristic optimization algorithms hybridized with artificial intelligence model for soil temperature prediction: Novel model. IEEE Access 8: 51884-51904. https://10.1109/ACCESS.2020.2979822
- Ren C, Liang Y J, Lu X J, & Yan H B (2019). Research on the soil moisture sliding estimation method using the LS-SVM based on multisatellite fusion. International Journal of Remote Sensing 40(5-6): 2104-2119. https://doi.org/10.1080/01431161.2018.1475778
- Republic of Turkey Ministry of Agriculture and Forestry (2021). Technical instruction of soil and land classification standards. Retrieved in January, 12, 2021 from https://www.tarimorman.gov.tr/ (In Turkish)
- Shamshirband S, Esmaeilbeiki F, Zarehaghi D, Neyshabouri M, Samadianfard S, Ghorbani M A, Mosavi A, Nabipour N & Chau K W (2020). Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths. Engineering Applications of Computational Fluid Mechanics 14(1): 939-953. https://doi.org/10.1080/19942060.2020.1788644
- Tabari H, Sabziparvar A A & Ahmadi M (2011). Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorology and Atmospheric Physics 110(3): 135-142. https://doi.org/10.1007/s00703-010-0110-z
- Turkish State Meteorological Service (2021). Climate status of Izmir province. Retrieved in January, 10, 2021 from https://izmir.mgm.gov.tr/ (In Turkish)
- Xing L, Li L, Gong J, Ren C, Liu J & Chen H (2018). Daily soil temperatures predictions for various climates in United States using datadriven model. Energy 160: 430-440. https://doi.org/10.1016/j.energy.2018.07.004
- Yang H, Zhou J, Feng J, Zhai S, Chen W, Liu J & Bian X (2019). Ditch-buried straw return: A novel tillage practice combined with tillage rotation and deep ploughing in rice-wheat rotation systems. Advances in Agronomy 154: 257-290. https://doi.org/10.1016/bs.agron.2018.11.004
- Yener D, Ozgener O & Ozgener L (2017). Prediction of soil temperatures for shallow geothermal applications in Turkey. Renewable and Sustainable Energy Reviews 70: 71-77. https://doi.org/10.1016/j.rser.2016.11.065
- Zhu X & Goldberg A B (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 3(1): 1-130. https://doi.org/10.2200/S00196ED1V01Y200906AIM006
| APA | TÜYSÜZOĞLU G, Birant D, KIRANOGLU V (2022). Soil Temperature Prediction via Self-Training: Izmir Case. , 47 - 62. 10.15832/ankutbd.775847 |
| Chicago | TÜYSÜZOĞLU GÖKSU,Birant Derya,KIRANOGLU Volkan Soil Temperature Prediction via Self-Training: Izmir Case. (2022): 47 - 62. 10.15832/ankutbd.775847 |
| MLA | TÜYSÜZOĞLU GÖKSU,Birant Derya,KIRANOGLU Volkan Soil Temperature Prediction via Self-Training: Izmir Case. , 2022, ss.47 - 62. 10.15832/ankutbd.775847 |
| AMA | TÜYSÜZOĞLU G,Birant D,KIRANOGLU V Soil Temperature Prediction via Self-Training: Izmir Case. . 2022; 47 - 62. 10.15832/ankutbd.775847 |
| Vancouver | TÜYSÜZOĞLU G,Birant D,KIRANOGLU V Soil Temperature Prediction via Self-Training: Izmir Case. . 2022; 47 - 62. 10.15832/ankutbd.775847 |
| IEEE | TÜYSÜZOĞLU G,Birant D,KIRANOGLU V "Soil Temperature Prediction via Self-Training: Izmir Case." , ss.47 - 62, 2022. 10.15832/ankutbd.775847 |
| ISNAD | TÜYSÜZOĞLU, GÖKSU vd. "Soil Temperature Prediction via Self-Training: Izmir Case". (2022), 47-62. https://doi.org/10.15832/ankutbd.775847 |
| APA | TÜYSÜZOĞLU G, Birant D, KIRANOGLU V (2022). Soil Temperature Prediction via Self-Training: Izmir Case. Tarım Bilimleri Dergisi, 28(1), 47 - 62. 10.15832/ankutbd.775847 |
| Chicago | TÜYSÜZOĞLU GÖKSU,Birant Derya,KIRANOGLU Volkan Soil Temperature Prediction via Self-Training: Izmir Case. Tarım Bilimleri Dergisi 28, no.1 (2022): 47 - 62. 10.15832/ankutbd.775847 |
| MLA | TÜYSÜZOĞLU GÖKSU,Birant Derya,KIRANOGLU Volkan Soil Temperature Prediction via Self-Training: Izmir Case. Tarım Bilimleri Dergisi, vol.28, no.1, 2022, ss.47 - 62. 10.15832/ankutbd.775847 |
| AMA | TÜYSÜZOĞLU G,Birant D,KIRANOGLU V Soil Temperature Prediction via Self-Training: Izmir Case. Tarım Bilimleri Dergisi. 2022; 28(1): 47 - 62. 10.15832/ankutbd.775847 |
| Vancouver | TÜYSÜZOĞLU G,Birant D,KIRANOGLU V Soil Temperature Prediction via Self-Training: Izmir Case. Tarım Bilimleri Dergisi. 2022; 28(1): 47 - 62. 10.15832/ankutbd.775847 |
| IEEE | TÜYSÜZOĞLU G,Birant D,KIRANOGLU V "Soil Temperature Prediction via Self-Training: Izmir Case." Tarım Bilimleri Dergisi, 28, ss.47 - 62, 2022. 10.15832/ankutbd.775847 |
| ISNAD | TÜYSÜZOĞLU, GÖKSU vd. "Soil Temperature Prediction via Self-Training: Izmir Case". Tarım Bilimleri Dergisi 28/1 (2022), 47-62. https://doi.org/10.15832/ankutbd.775847 |
