vetpressАграрная наукаAgrarian science0869-81552686-701XРедакция журнала "Аграрная наука"10.32634/0869-8155-2025-393-04-167-171vetpress-3590Research ArticleЦИФРОВИЗАЦИЯ АПКDIGITALIZATION OF THE AGRO-INDUSTRIAL COMPLEXПрименение нейросетей и технологий больших данных в сельском хозяйстве: повышение эффективности и устойчивости агропроизводстваApplication of neural networks and big data technologies in agriculture: increasing the efficiency and sustainability of agricultural productionГалкинА. 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 Federation20252504202514167171Copyright © 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/3590

Данная статья посвящена исследованию потенциала применения нейросетей и технологий больших данных в сельском хозяйстве для повышения эффективности и устойчивости агропроизводства. На основе комплексного анализа научной литературы и эмпирических данных из реальных проектов внедрения выявлены ключевые направления использования этих инновационных подходов: точное земледелие, оптимизация управления ресурсами, мониторинг состояния посевов и животных, прогнозирование урожайности и продуктивности. Показано, что интеграция нейросетевых алгоритмов и инструментов анализа больших данных позволяет существенно улучшить процесс принятия решений на всех этапах сельхозпроизводства за счет учета множества факторов и выявления неочевидных закономерностей. Разработана концептуальная модель системы поддержки принятия решений для агропредприятий, основанная на синтезе методов машинного обучения и интеллектуального анализа разнородных массивов данных. Верификация модели на реальных датасетах продемонстрировала повышение точности прогнозов урожайности на 15–20% и снижение затрат ресурсов на 10–12% по сравнению с традиционными подходами. Полученные результаты создают основу для масштабирования предложенных решений и их адаптации под специфику конкретных агропредприятий с целью перехода к устойчивому и высокопродуктивному сельскому хозяйству нового поколения.

This article is dedicated to exploring the potential of applying neural networks and big data technologies in agriculture to enhance the efficiency and sustainability of agricultural production. Based on a comprehensive analysis of scientific literature and empirical data from real implementation projects, key areas for utilizing these innovative approaches have been identified: precision farming, resource management optimization, crop and livestock condition monitoring, and yield and productivity forecasting. It has been demonstrated that integrating neural network algorithms and big data analysis tools significantly improves the decision-making process at all stages of agricultural production by accounting for numerous factors and identifying non-obvious patterns. A conceptual model of a decision support system for agricultural enterprises has been developed, based on synthesizing machine learning methods and intelligent analysis of heterogeneous data sets. Validation of the model on real datasets showed a 15–20% improvement in yield prediction accuracy and a 10–12% reduction in resource costs compared to traditional approaches. The results lay the foundation for scaling the proposed solutions and adapting them to the specific characteristics of individual agricultural enterprises, aiming for a transition to sustainable and highly productive next-generation agriculture.

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