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...

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Vydané v:IEEE access Ročník 12; s. 19902 - 19910
Hlavní autori: Tussupov, Jamalbek, Yessenova, Moldir, Abdikerimova, Gulzira, Aimbetov, Aidyn, Baktybekov, Kazbek, Murzabekova, Gulden, Aitimova, Ulzada
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy metrics
Artificial neural networks
Brightness
classification
clustering
Clustering algorithms
Coordinates
Crop production
Crops
Data analysis
Data models
data verification
Disease control
Diseases
Earth
Formal verification
Machine learning
Monitoring
New technology
Pests
Remote sensing
Satellite imagery
Spectral analysis
spectral brightness coefficient
Vegetation mapping
Verification
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3361046