vetpressАграрная наукаAgrarian science0869-81552686-701XРедакция журнала "Аграрная наука"10.32634/0869-8155-2025-394-05-171-174vetpress-3637Research ArticleЦИФРОВИЗАЦИЯ АПКDIGITALIZATION OF THE AGRO-INDUSTRIAL COMPLEXПрименение методов машинного обучения и анализа больших данных в точном земледелииApplication of machine learning methods and big data analysis in precision agricultureГалкинА. И.GalkinA. I.

Андрей Игоревич Галкин, кандидат экономических наук, доцент кафедры

Ленинградский пр-т, 49/2, Москва, 125167

Andrey Igorevich Galkin, Candidate of Economic Sciences, Associate Professor of the Department

49/2 Leningradsky Ave., Moscow, 125167

aigalkin@fa.ruФинансовый университет при Правительстве Российской ФедерацииРоссияFinancial University under the Government of the Russian FederationRussian Federation20251605202505171174Copyright © Галкин А.И., 20252025Галкин А.И.Galkin A.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vetpress.ru/jour/article/view/3637Актуальность

Актуальность. Современные технологии сбора и анализа данных открывают новые возможности для повышения эффективности и устойчивости сельскохозяйственного производства. Данная работа посвящена исследованию потенциала применения методов машинного обучения и анализа больших данных в точном земледелии.

Методы

Методы. На основе систематического обзора литературы выделены ключевые направления использования этих подходов: оптимизация внесения удобрений и ирригации, раннее выявление болезней и вредителей, прогнозирование урожайности. С использованием методов регрессионного анализа, классификации и кластеризации на выборке данных полевых измерений за 2018–2023 гг. на примере производства пшеницы в условиях Центрально-Чернозёмного региона РФ показано, что применение предложенных алгоритмов позволяет повысить урожайность на 12–17% при снижении затрат удобрений на 10–14%. Предложена концептуальная модель интеллектуальной системы поддержки принятия решений для точного земледелия. Обсуждаются вопросы масштабирования подхода и его адаптации к другим культурам и регионам.

Результаты исследования демонстрируют значительный потенциал применения передовых методов анализа данных для повышения эффективности и экологичности растениеводства.

Relevance

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

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

Results. The research results demonstrate the significant potential of advanced data analysis methods to enhance the efficiency and environmental sustainability of crop production.

точное земледелиемашинное обучениебольшие данныеурожайностьустойчивое развитиеprecision farmingmachine learningbig datayieldsustainable developmentReferencesWolfert S., Ge L., Verdouw C., Bogaardt M.-J. Big Data in Smart Farming — a review. Agricultural Systems. 2017; 153: 69–80. https://doi.org/10.1016/j.agsy.2017.01.023Wolfert S., Ge L., Verdouw C., Bogaardt M.-J. Big Data in Smart Farming — a review. Agricultural Systems. 2017; 153: 69–80. https://doi.org/10.1016/j.agsy.2017.01.023Liakos K.G., Busato P., Moshou D., Pearson S., Bochtis D. Machine Learning in Agriculture: a Review. Sensors. 2018; 18(8): 2674. https://doi.org/10.3390/s18082674Liakos K.G., Busato P., Moshou D., Pearson S., Bochtis D. Machine Learning in Agriculture: a Review. Sensors. 2018; 18(8): 2674. https://doi.org/10.3390/s18082674Chlingaryan A., Sukkarieh S., Whelan B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Computers and Electronics . in griculture. 2018; 151: 61–69. https://doi.org/10.1016/j.compag.2018.05.012Chlingaryan A., Sukkarieh S., Whelan B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Computers and Electronics . in griculture. 2018; 151: 61–69. https://doi.org/10.1016/j.compag.2018.05.012Kamilaris A., Prenafeta-Boldú F.X. Deep learning in agriculture: a survey. Computers and Electronics in Agriculture. 2018; 147: 70–90. https://doi.org/10.1016/j.compag.2018.02.016Kamilaris A., Prenafeta-Boldú F.X. Deep learning in agriculture: a survey. Computers and Electronics in Agriculture. 2018; 147: 70–90. https://doi.org/10.1016/j.compag.2018.02.016Pantazi X.E., Moshou D., Alexandridis T., Whetton R.L., Mouazen A.M. Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics . in Agriculture. 2016; 121: 57–65. https://doi.org/10.1016/j.compag.2015.11.018Pantazi X.E., Moshou D., Alexandridis T., Whetton R.L., Mouazen A.M. Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics . in Agriculture. 2016; 121: 57–65. https://doi.org/10.1016/j.compag.2015.11.018Basso B., Fiorentino C., Cammarano D., Schulthess U. Variable rate nitrogen fertilizer response in wheat using remote sensing. Precision Agriculture. 2016; 17(2): 168–182. https://doi.org/10.1007/s11119-015-9414-9Basso B., Fiorentino C., Cammarano D., Schulthess U. Variable rate nitrogen fertilizer response in wheat using remote sensing. Precision Agriculture. 2016; 17(2): 168–182. https://doi.org/10.1007/s11119-015-9414-9Weiss M., Jacob F., Duveiller G. Remote sensing for agricultural applications: a meta-review. Remote Sensing of Environment. 2020; 236: 111402. https://doi.org/10.1016/j.rse.2019.111402Weiss M., Jacob F., Duveiller G. Remote sensing for agricultural applications: a meta-review. Remote Sensing of Environment. 2020; 236: 111402. https://doi.org/10.1016/j.rse.2019.111402Sharma A., Jain A., Gupta P., Chowdary V. Machine Learning Applications for Precision Agriculture: a Comprehensive Review. IEEE Access. 2021; 9: 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415Sharma A., Jain A., Gupta P., Chowdary V. Machine Learning Applications for Precision Agriculture: a Comprehensive Review. IEEE Access. 2021; 9: 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415Zha H. et al. Improving crop yields and nitrogen use efficiency using an unmanned aerial vehicle-based low-altitude remote sensing technology. IEEE Access. 2020; 8: 57187–57199. https://doi.org/10.1109/ACCESS.2020.2982100Zha H. et al. Improving crop yields and nitrogen use efficiency using an unmanned aerial vehicle-based low-altitude remote sensing technology. IEEE Access. 2020; 8: 57187–57199. https://doi.org/10.1109/ACCESS.2020.2982100Pivoto D. et al. Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review. 2019; 22(4): 571–588. https://doi.org/10.22434/IFAMR2018.0086Pivoto D. et al. Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review. 2019; 22(4): 571–588. https://doi.org/10.22434/IFAMR2018.0086

The authors declare that there are no conflicts of interest present.