Data mining for assessing soil fertility
The study is devoted to the use of data mining to assess soil fertility, which is a modern and effective tool in agriculture and ecology. The method includes integrated approaches to data collection, processing and analysis aimed at determining soil fertility, its composition and potential for succe...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/24/e3sconf_aees2024_02012.pdf |
| _version_ | 1797299432938536960 |
|---|---|
| author | Inoyatova Manzura Ziyadullaev Davron Muhamediyeva Dilnoz Aynaqulov Sharofiddin Ziyaeva Sholpan |
| author_facet | Inoyatova Manzura Ziyadullaev Davron Muhamediyeva Dilnoz Aynaqulov Sharofiddin Ziyaeva Sholpan |
| author_sort | Inoyatova Manzura |
| collection | DOAJ |
| description | The study is devoted to the use of data mining to assess soil fertility, which is a modern and effective tool in agriculture and ecology. The method includes integrated approaches to data collection, processing and analysis aimed at determining soil fertility, its composition and potential for successful agricultural use. Using a variety of machine learning techniques and statistical models, researchers can predict crop yields, optimize fertilization and soil management strategies, and identify environmental and soil health risks. In particular, the use of the regression method makes it possible to build models that predict the values of fertile soil parameters based on available data. Using machine learning techniques such as Bayes' theorem and support vector machines (SVM), researchers can effectively estimate soil fertility, predict soil characteristics, and optimize agricultural practices. The results of the study demonstrate the high performance of the models in soil sample classification tasks, highlighting their potential for improving soil resource management and increasing crop yields. Such machine learning techniques provide powerful tools for agricultural workers and researchers, facilitating more precise and sustainable agriculture, which is essential for food security and ecosystem resilience. |
| first_indexed | 2024-03-07T22:50:38Z |
| format | Article |
| id | doaj.art-90773e746dd3488a8aa44ba4509a823a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-07T22:50:38Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-90773e746dd3488a8aa44ba4509a823a2024-02-23T10:28:19ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014940201210.1051/e3sconf/202449402012e3sconf_aees2024_02012Data mining for assessing soil fertilityInoyatova Manzura0Ziyadullaev Davron1Muhamediyeva Dilnoz2Aynaqulov Sharofiddin3Ziyaeva Sholpan4National 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"National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute"The study is devoted to the use of data mining to assess soil fertility, which is a modern and effective tool in agriculture and ecology. The method includes integrated approaches to data collection, processing and analysis aimed at determining soil fertility, its composition and potential for successful agricultural use. Using a variety of machine learning techniques and statistical models, researchers can predict crop yields, optimize fertilization and soil management strategies, and identify environmental and soil health risks. In particular, the use of the regression method makes it possible to build models that predict the values of fertile soil parameters based on available data. Using machine learning techniques such as Bayes' theorem and support vector machines (SVM), researchers can effectively estimate soil fertility, predict soil characteristics, and optimize agricultural practices. The results of the study demonstrate the high performance of the models in soil sample classification tasks, highlighting their potential for improving soil resource management and increasing crop yields. Such machine learning techniques provide powerful tools for agricultural workers and researchers, facilitating more precise and sustainable agriculture, which is essential for food security and ecosystem resilience.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/24/e3sconf_aees2024_02012.pdf |
| spellingShingle | Inoyatova Manzura Ziyadullaev Davron Muhamediyeva Dilnoz Aynaqulov Sharofiddin Ziyaeva Sholpan Data mining for assessing soil fertility E3S Web of Conferences |
| title | Data mining for assessing soil fertility |
| title_full | Data mining for assessing soil fertility |
| title_fullStr | Data mining for assessing soil fertility |
| title_full_unstemmed | Data mining for assessing soil fertility |
| title_short | Data mining for assessing soil fertility |
| title_sort | data mining for assessing soil fertility |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/24/e3sconf_aees2024_02012.pdf |
| work_keys_str_mv | AT inoyatovamanzura dataminingforassessingsoilfertility AT ziyadullaevdavron dataminingforassessingsoilfertility AT muhamediyevadilnoz dataminingforassessingsoilfertility AT aynaqulovsharofiddin dataminingforassessingsoilfertility AT ziyaevasholpan dataminingforassessingsoilfertility |