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Year 2022, Volume: 28 Issue: 1, 47 - 62, 25.02.2022
https://doi.org/10.15832/ankutbd.775847

Abstract

References

  • Alizamir M, Kisi O, Ahmed A N, Mert C, Fai C M, Kim S, Kim N W & El-Shafie A (2020). Advanced machine learning model for better prediction accuracy of soil temperature at different depths. Plos One 15(4): e0231055
  • 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
  • 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
  • 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
  • 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
  • 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
  • Ji W, Liu Y & Zhen J Q (2020). Prediction of soil humidity based on random weight Particle Swarm Optimized Extreme Learning Machine. Journal of Physics: Conference Series 1486: 042043
  • 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
  • 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
  • Pekel E (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology 139(3): 1111-1119
  • 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
  • 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 multi-satellite fusion. International Journal of Remote Sensing 40(5-6): 2104-2119
  • Sanikhani H, Deo R C, Yaseen Z M, Eray O & Kisi O (2018). Non-tuned data intelligent model for soil temperature estimation: A new approach. Geoderma 330: 52-64
  • Zhu X & Goldberg A B (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 3(1): 1-130

Soil Temperature Prediction via Self-Training: Izmir Case

Year 2022, Volume: 28 Issue: 1, 47 - 62, 25.02.2022
https://doi.org/10.15832/ankutbd.775847

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.

References

  • Alizamir M, Kisi O, Ahmed A N, Mert C, Fai C M, Kim S, Kim N W & El-Shafie A (2020). Advanced machine learning model for better prediction accuracy of soil temperature at different depths. Plos One 15(4): e0231055
  • 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
  • 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
  • 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
  • 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
  • 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
  • Ji W, Liu Y & Zhen J Q (2020). Prediction of soil humidity based on random weight Particle Swarm Optimized Extreme Learning Machine. Journal of Physics: Conference Series 1486: 042043
  • 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
  • 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
  • Pekel E (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology 139(3): 1111-1119
  • 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
  • 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 multi-satellite fusion. International Journal of Remote Sensing 40(5-6): 2104-2119
  • Sanikhani H, Deo R C, Yaseen Z M, Eray O & Kisi O (2018). Non-tuned data intelligent model for soil temperature estimation: A new approach. Geoderma 330: 52-64
  • Zhu X & Goldberg A B (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 3(1): 1-130
There are 14 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Göksu Tüysüzoğlu 0000-0002-2926-4267

Derya Birant 0000-0003-3138-0432

Volkan Kıranoglu 0000-0003-3864-519X

Publication Date February 25, 2022
Submission Date July 30, 2020
Acceptance Date March 9, 2021
Published in Issue Year 2022 Volume: 28 Issue: 1

Cite

APA Tüysüzoğlu, G., Birant, D., & Kıranoglu, V. (2022). Soil Temperature Prediction via Self-Training: Izmir Case. Journal of Agricultural Sciences, 28(1), 47-62. https://doi.org/10.15832/ankutbd.775847

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