Exploring the relationship between soil chemical composition and NDVI index using AI
This scientific article presents the results of research focused on developing a method for predicting the Normalized Difference Vegetation Index (NDVI) based on soil chemical composition using a multilayer artificial intelligence (AI) model. This method aims to improve the accuracy and predictive c...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/99/e3sconf_afe23_03041.pdf |
| _version_ | 1797345117534683136 |
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| author | Lebedev Ivan Ogorodnikov Sergey |
| author_facet | Lebedev Ivan Ogorodnikov Sergey |
| author_sort | Lebedev Ivan |
| collection | DOAJ |
| description | This scientific article presents the results of research focused on developing a method for predicting the Normalized Difference Vegetation Index (NDVI) based on soil chemical composition using a multilayer artificial intelligence (AI) model. This method aims to improve the accuracy and predictive capability of land resource assessment, as well as the impact of chemical factors on vegetation. The study involved collecting soil chemical composition data in various conditions, providing a wide range of information for analysis. For NDVI assessment, a key indicator of vegetation condition, data from modern Earth observation satellite systems were used. The central aspect of the research is the multilayer AI model based on the Rosenblatt perceptron, capable of detecting complex nonlinear relationships between soil chemical parameters and NDVI. The training algorithm was tuned for maximum accuracy and generalization of results. The results show that the developed model provides high accuracy in NDVI predictions, making it an important tool for agriculture, ecology, and sustainable land use. These findings highlight the potential of using AI and soil data to optimize agricultural production, monitor ecosystems, and manage land resources. |
| first_indexed | 2024-03-08T11:12:51Z |
| format | Article |
| id | doaj.art-da174c96c16744d5a0ce7ae73a48ea48 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:12:51Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-da174c96c16744d5a0ce7ae73a48ea482024-01-26T10:40:49ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014620304110.1051/e3sconf/202346203041e3sconf_afe23_03041Exploring the relationship between soil chemical composition and NDVI index using AILebedev Ivan0Ogorodnikov Sergey1Moscow Aviation Institute (National Research University)Moscow Aviation Institute (National Research University)This scientific article presents the results of research focused on developing a method for predicting the Normalized Difference Vegetation Index (NDVI) based on soil chemical composition using a multilayer artificial intelligence (AI) model. This method aims to improve the accuracy and predictive capability of land resource assessment, as well as the impact of chemical factors on vegetation. The study involved collecting soil chemical composition data in various conditions, providing a wide range of information for analysis. For NDVI assessment, a key indicator of vegetation condition, data from modern Earth observation satellite systems were used. The central aspect of the research is the multilayer AI model based on the Rosenblatt perceptron, capable of detecting complex nonlinear relationships between soil chemical parameters and NDVI. The training algorithm was tuned for maximum accuracy and generalization of results. The results show that the developed model provides high accuracy in NDVI predictions, making it an important tool for agriculture, ecology, and sustainable land use. These findings highlight the potential of using AI and soil data to optimize agricultural production, monitor ecosystems, and manage land resources.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/99/e3sconf_afe23_03041.pdf |
| spellingShingle | Lebedev Ivan Ogorodnikov Sergey Exploring the relationship between soil chemical composition and NDVI index using AI E3S Web of Conferences |
| title | Exploring the relationship between soil chemical composition and NDVI index using AI |
| title_full | Exploring the relationship between soil chemical composition and NDVI index using AI |
| title_fullStr | Exploring the relationship between soil chemical composition and NDVI index using AI |
| title_full_unstemmed | Exploring the relationship between soil chemical composition and NDVI index using AI |
| title_short | Exploring the relationship between soil chemical composition and NDVI index using AI |
| title_sort | exploring the relationship between soil chemical composition and ndvi index using ai |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/99/e3sconf_afe23_03041.pdf |
| work_keys_str_mv | AT lebedevivan exploringtherelationshipbetweensoilchemicalcompositionandndviindexusingai AT ogorodnikovsergey exploringtherelationshipbetweensoilchemicalcompositionandndviindexusingai |