Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods
This article is devoted to a set of important areas of research: the analysis of formal representations and verification of pests and pathogens affecting crops using spectral brightness coefficients (SBR) for the period from 2021 to 2023. The database contains about 10,000 records covering the growi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418240/ |
| _version_ | 1797319539961102336 |
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| author | Jamalbek Tussupov Moldir Yessenova Gulzira Abdikerimova Aidyn Aimbetov Kazbek Baktybekov Gulden Murzabekova Ulzada Aitimova |
| author_facet | Jamalbek Tussupov Moldir Yessenova Gulzira Abdikerimova Aidyn Aimbetov Kazbek Baktybekov Gulden Murzabekova Ulzada Aitimova |
| author_sort | Jamalbek Tussupov |
| collection | DOAJ |
| description | This article is devoted to a set of important areas of research: the analysis of formal representations and verification of pests and pathogens affecting crops using spectral brightness coefficients (SBR) for the period from 2021 to 2023. The database contains about 10,000 records covering the growing season, types of diseases and pests, as well as their growth phases in a real coordinate system. The work uses machine learning techniques including logistic regression, extreme gradient boosting (XGBoost), and Vanilla convolutional neural network (CNN) to analyze spectral data and classify the presence of pests and diseases in satellite images. The main goal of the work is to optimize and improve the quality of agricultural productivity through early detection and accurate classification of pests and diseases in the agricultural sector. The results of the study can be applied in the development of innovative agricultural systems that will increase yields, reduce the cost of pest and disease control, and optimize production processes. The conclusions of this work can be used both as scientific and practical recommendations for agricultural enterprises and organizations and for the development of new technologies and programs for automating agricultural processes. The use of machine learning techniques and spectral data analysis promises significant breakthroughs in the agricultural sector, helping to improve the efficiency, sustainability, and quality of crop production. |
| first_indexed | 2024-03-08T04:08:23Z |
| format | Article |
| id | doaj.art-3dc0c237df574b3fb09ef976f9a7a0d0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:08:23Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3dc0c237df574b3fb09ef976f9a7a0d02024-02-09T00:01:31ZengIEEEIEEE Access2169-35362024-01-0112199021991010.1109/ACCESS.2024.336104610418240Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning MethodsJamalbek Tussupov0https://orcid.org/0000-0002-9179-0428Moldir Yessenova1https://orcid.org/0000-0002-2644-0966Gulzira Abdikerimova2Aidyn Aimbetov3Kazbek Baktybekov4https://orcid.org/0000-0002-6401-8053Gulden Murzabekova5https://orcid.org/0000-0001-9807-5200Ulzada Aitimova6https://orcid.org/0000-0002-0803-7137L. N. Gumilyov Eurasian National University, Astana, KazakhstanL. N. Gumilyov Eurasian National University, Astana, KazakhstanL. N. Gumilyov Eurasian National University, Astana, KazakhstanJSC National Company Kazakhstan Garysh Sapary, Astana, KazakhstanKazakh-French Space Enterprise Galam LLP, Astana, KazakhstanS. Seifullin Kazakh Agro Technical Research University, Astana, KazakhstanS. Seifullin Kazakh Agro Technical Research University, Astana, KazakhstanThis article is devoted to a set of important areas of research: the analysis of formal representations and verification of pests and pathogens affecting crops using spectral brightness coefficients (SBR) for the period from 2021 to 2023. The database contains about 10,000 records covering the growing season, types of diseases and pests, as well as their growth phases in a real coordinate system. The work uses machine learning techniques including logistic regression, extreme gradient boosting (XGBoost), and Vanilla convolutional neural network (CNN) to analyze spectral data and classify the presence of pests and diseases in satellite images. The main goal of the work is to optimize and improve the quality of agricultural productivity through early detection and accurate classification of pests and diseases in the agricultural sector. The results of the study can be applied in the development of innovative agricultural systems that will increase yields, reduce the cost of pest and disease control, and optimize production processes. The conclusions of this work can be used both as scientific and practical recommendations for agricultural enterprises and organizations and for the development of new technologies and programs for automating agricultural processes. The use of machine learning techniques and spectral data analysis promises significant breakthroughs in the agricultural sector, helping to improve the efficiency, sustainability, and quality of crop production.https://ieeexplore.ieee.org/document/10418240/Accuracy metricsclassificationclusteringdata verificationmachine learningspectral brightness coefficient |
| spellingShingle | Jamalbek Tussupov Moldir Yessenova Gulzira Abdikerimova Aidyn Aimbetov Kazbek Baktybekov Gulden Murzabekova Ulzada Aitimova Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods IEEE Access Accuracy metrics classification clustering data verification machine learning spectral brightness coefficient |
| title | Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods |
| title_full | Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods |
| title_fullStr | Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods |
| title_full_unstemmed | Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods |
| title_short | Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods |
| title_sort | analysis of formal concepts for verification of pests and diseases of crops using machine learning methods |
| topic | Accuracy metrics classification clustering data verification machine learning spectral brightness coefficient |
| url | https://ieeexplore.ieee.org/document/10418240/ |
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