Application of machine learning methods and big data analysis in precision agriculture
https://doi.org/10.32634/0869-8155-2025-394-05-171-174
Abstract
Relevance. Modern technologies for data collection and analysis open up new opportunities for improving the efficiency and sustainability of agricultural production. This work is dedicated to exploring the potential of applying machine learning methods and big data analysis in precision agriculture.
Methods. Based on a systematic literature review, key areas of application for these approaches are identified: optimization of fertilizer and irrigation use, early detection of diseases and pests, and yield prediction. Using regression analysis, classification, and clustering methods on a dataset of field measurements from 2018–2023, demonstrated on wheat production in the Central Black Earth region of the Russian Federation, it is shown that the application of the proposed algorithms can increase yield by 12–17% while reducing fertilizer costs by 10– 14%. a conceptual model for an intelligent decision support system for precision agriculture is proposed. Issues of scaling the approach and its adaptation to other crops and regions are discussed.
Results. The research results demonstrate the significant potential of advanced data analysis methods to enhance the efficiency and environmental sustainability of crop production.
About the Author
A. I. Galkin
Financial University under the Government of the Russian Federation
Russian Federation
Russian Federation
Andrey Igorevich Galkin, Candidate of Economic Sciences, Associate Professor of the Department
49/2 Leningradsky Ave., Moscow, 125167
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Review
For citations:
Galkin A.I. Application of machine learning methods and big data analysis in precision agriculture. Agrarian science. 2025;(5):171-174. (In Russ.) https://doi.org/10.32634/0869-8155-2025-394-05-171-174
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