A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)
Soil temperature prediction is an important task since soil temperature plays an important role in agriculture and land use. Although some progress has been made in this area, the existing methods provide a regression or nominal classification task. However, ordinal classification is yet to be explo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ankara University
2022-10-01
|
| Series: | Journal of Agricultural Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1525389 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850236958914117632 |
|---|---|
| author | Derya Birant Pelin Yıldırım Taşer Cansel Küçük |
| author_facet | Derya Birant Pelin Yıldırım Taşer Cansel Küçük |
| author_sort | Derya Birant |
| collection | DOAJ |
| description | Soil temperature prediction is an important task since soil temperature plays an important role in agriculture and land use. Although some progress has been made in this area, the existing methods provide a regression or nominal classification task. However, ordinal classification is yet to be explored. To bridge the gap, this paper proposes a novel approach: Soil Temperature Ordinal Classification (STOC), which considers the relationships between the class labels during soil temperature level prediction. To demonstrate the effectiveness of the proposed approach, the STOC method using five different traditional machine learning methods (Decision Tree, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, and Random Forest) was applied on daily values of meteorological and soil data obtained from 16 stations in three states (Utah, Alabama, and New Mexico) of United States at five soil depths (2, 4, 8, 20, and 40 inches) between the years of 2011 and 2020. The experiments show that the proposed STOC approach is an efficient method for soil temperature level (very low, low, medium, high, and very high) prediction. The applied STOC models (STOC.DT, STOC.NB, STOC.KNN, STOC.SVM, and STOC.RF) showed average accuracy rates of 90.95%, 77.09%, 90.84%, 89.94%, and 90.91% on the experimental datasets, respectively. It was observed from the experimental results that the STOC.DT method achieved the best soil temperature level prediction among the others. |
| format | Article |
| id | doaj-art-2be4641f7f084371a93dbbd06c79164c |
| institution | OA Journals |
| issn | 1300-7580 2148-9297 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | Ankara University |
| record_format | Article |
| series | Journal of Agricultural Sciences |
| spelling | doaj-art-2be4641f7f084371a93dbbd06c79164c2025-08-20T02:01:51ZengAnkara UniversityJournal of Agricultural Sciences1300-75802148-92972022-10-0128463564910.15832/ankutbd.86604545A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)Derya Birant0Pelin Yıldırım Taşer1Cansel Küçük2Dokuz Eylul University, Department of Computer EngineeringIzmir Bakircay University, Department of Computer EngineeringDokuz Eylul University, Graduate School of Natural and Applied SciencesSoil temperature prediction is an important task since soil temperature plays an important role in agriculture and land use. Although some progress has been made in this area, the existing methods provide a regression or nominal classification task. However, ordinal classification is yet to be explored. To bridge the gap, this paper proposes a novel approach: Soil Temperature Ordinal Classification (STOC), which considers the relationships between the class labels during soil temperature level prediction. To demonstrate the effectiveness of the proposed approach, the STOC method using five different traditional machine learning methods (Decision Tree, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, and Random Forest) was applied on daily values of meteorological and soil data obtained from 16 stations in three states (Utah, Alabama, and New Mexico) of United States at five soil depths (2, 4, 8, 20, and 40 inches) between the years of 2011 and 2020. The experiments show that the proposed STOC approach is an efficient method for soil temperature level (very low, low, medium, high, and very high) prediction. The applied STOC models (STOC.DT, STOC.NB, STOC.KNN, STOC.SVM, and STOC.RF) showed average accuracy rates of 90.95%, 77.09%, 90.84%, 89.94%, and 90.91% on the experimental datasets, respectively. It was observed from the experimental results that the STOC.DT method achieved the best soil temperature level prediction among the others.https://dergipark.org.tr/tr/download/article-file/1525389agricultureclassificationdecision treemachine learningrandom forestsoil temperature level |
| spellingShingle | Derya Birant Pelin Yıldırım Taşer Cansel Küçük A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC) Journal of Agricultural Sciences agriculture classification decision tree machine learning random forest soil temperature level |
| title | A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC) |
| title_full | A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC) |
| title_fullStr | A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC) |
| title_full_unstemmed | A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC) |
| title_short | A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC) |
| title_sort | novel machine learning approach soil temperature ordinal classification stoc |
| topic | agriculture classification decision tree machine learning random forest soil temperature level |
| url | https://dergipark.org.tr/tr/download/article-file/1525389 |
| work_keys_str_mv | AT deryabirant anovelmachinelearningapproachsoiltemperatureordinalclassificationstoc AT pelinyıldırımtaser anovelmachinelearningapproachsoiltemperatureordinalclassificationstoc AT canselkucuk anovelmachinelearningapproachsoiltemperatureordinalclassificationstoc AT deryabirant novelmachinelearningapproachsoiltemperatureordinalclassificationstoc AT pelinyıldırımtaser novelmachinelearningapproachsoiltemperatureordinalclassificationstoc AT canselkucuk novelmachinelearningapproachsoiltemperatureordinalclassificationstoc |