Ensemble data mining methods for assessing soil fertility
The application of ensemble data mining methods in assessing soil fertility and the use of methods such as random forest, gradient boosting and bagging to determine the level of soil fertility are examined in the article. Ensemble methods combine multiple machine learning models to improve the accur...
| Váldodahkkit: | , , , , , |
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| Materiálatiipa: | Artihkal |
| Giella: | English |
| Almmustuhtton: |
EDP Sciences
2024-01-01
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| Ráidu: | E3S Web of Conferences |
| Liŋkkat: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/24/e3sconf_aees2024_02013.pdf |
| _version_ | 1827344648611299328 |
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| author | Ziyadullaev Davron Muhamediyeva Dilnoz Khujamkulova Khosiyat Abdurakhimov Doniyor Maksumkhanova Azizahon Ziyodullaeva Gulchiroy |
| author_facet | Ziyadullaev Davron Muhamediyeva Dilnoz Khujamkulova Khosiyat Abdurakhimov Doniyor Maksumkhanova Azizahon Ziyodullaeva Gulchiroy |
| author_sort | Ziyadullaev Davron |
| collection | DOAJ |
| description | The application of ensemble data mining methods in assessing soil fertility and the use of methods such as random forest, gradient boosting and bagging to determine the level of soil fertility are examined in the article. Ensemble methods combine multiple machine learning models to improve the accuracy and stability of estimates. These methods consider various factors, including soil chemistry, climatic conditions, and historical crop yield data. The study also examines the application of the decision tree algorithm and such methods as random forest and bagging to estimate soil fertility. Performance results of these methods are provided using precision, recall, and F1-measure metrics. The results obtained show the high performance of ensemble methods in the task of classifying soil fertility levels. They have important implications for agricultural farms and research organizations that are working to improve soil management and increase crop yields. |
| first_indexed | 2024-03-07T22:50:47Z |
| format | Article |
| id | doaj.art-c099d16c0ab64ff7a35be5ee5196e13d |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-07T22:50:47Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c099d16c0ab64ff7a35be5ee5196e13d2024-02-23T10:28:19ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014940201310.1051/e3sconf/202449402013e3sconf_aees2024_02013Ensemble data mining methods for assessing soil fertilityZiyadullaev Davron0Muhamediyeva Dilnoz1Khujamkulova Khosiyat2Abdurakhimov DoniyorMaksumkhanova Azizahon3Ziyodullaeva Gulchiroy4National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute"National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute"National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute"National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute"Tashkent University of Information Technologies named after Mukhammad al-KhwarizmiThe application of ensemble data mining methods in assessing soil fertility and the use of methods such as random forest, gradient boosting and bagging to determine the level of soil fertility are examined in the article. Ensemble methods combine multiple machine learning models to improve the accuracy and stability of estimates. These methods consider various factors, including soil chemistry, climatic conditions, and historical crop yield data. The study also examines the application of the decision tree algorithm and such methods as random forest and bagging to estimate soil fertility. Performance results of these methods are provided using precision, recall, and F1-measure metrics. The results obtained show the high performance of ensemble methods in the task of classifying soil fertility levels. They have important implications for agricultural farms and research organizations that are working to improve soil management and increase crop yields.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/24/e3sconf_aees2024_02013.pdf |
| spellingShingle | Ziyadullaev Davron Muhamediyeva Dilnoz Khujamkulova Khosiyat Abdurakhimov Doniyor Maksumkhanova Azizahon Ziyodullaeva Gulchiroy Ensemble data mining methods for assessing soil fertility E3S Web of Conferences |
| title | Ensemble data mining methods for assessing soil fertility |
| title_full | Ensemble data mining methods for assessing soil fertility |
| title_fullStr | Ensemble data mining methods for assessing soil fertility |
| title_full_unstemmed | Ensemble data mining methods for assessing soil fertility |
| title_short | Ensemble data mining methods for assessing soil fertility |
| title_sort | ensemble data mining methods for assessing soil fertility |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/24/e3sconf_aees2024_02013.pdf |
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