Soil Temperature Prediction via Self-Training: Izmir Case

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dc.contributor.authorTüysüzoğlu, Göksu
dc.contributor.authorBirant, Derya
dc.contributor.authorKıranoğlu, Volkan
dc.contributor.departmentOthertr_TR
dc.contributor.facultyOthertr_TR
dc.date.accessioned2022-09-13T07:23:35Z
dc.date.available2022-09-13T07:23:35Z
dc.date.issued2022
dc.description.abstractThis 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.tr_TR
dc.description.indexWostr_TR
dc.description.indexScopustr_TR
dc.identifier.endpage62tr_TR
dc.identifier.issn/e-issn2148-9297
dc.identifier.issue01tr_TR
dc.identifier.startpage47tr_TR
dc.identifier.urihttps://doi.org/10.15832/ankutbd.775847tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/83948
dc.identifier.volume28tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesitr_TR
dc.relation.isversionof10.15832/ankutbd.775847tr_TR
dc.relation.journalTarım Bilimleri Dergisitr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıtr_TR
dc.subjectSoil temperature predictiontr_TR
dc.subjectSelf-trainingtr_TR
dc.subjectArtificial Intelligencetr_TR
dc.titleSoil Temperature Prediction via Self-Training: Izmir Casetr_TR
dc.typeArticletr_TR

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Cilt:28 Sayı:01 (2022)