vetpressАграрная наукаAgrarian science0869-81552686-701XРедакция журнала "Аграрная наука"10.32634/0869-8155-2025-392-03-150-154vetpress-3507Research ArticleРЕГИОНАЛЬНАЯ И ОТРАСЛЕВАЯ ЭКОНОМИКАREGIONAL AND SECTORAL ECONOMYПрименение больших данных и нейросетей в точном земледелии для повышения урожайности и устойчивости сельскохозяйственного производстваThe use of big data and neural networks in precision agriculture to increase crop yield and the sustainability of agricultural productionГалкинА. И.GalkinA. I.

Галкин Андрей Игоревич - кандидат экономических наук, доцент кафедры.

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

Andrey I. 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 Federation20252003202503150154Copyright © 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/3507

Статья посвящена анализу возможностей применения технологий больших данных и нейросетей в точном земледелии с целью повышения урожайности и устойчивости сельскохозяйственного производства. На основе обзора актуальных исследований выявлены ключевые тренды и пробелы в данной области. Предложена оригинальная методология, включающая сбор и интеграцию разнородных массивов данных (данные дистанционного зондирования, сенсорные данные, агрохимические показатели почв и др.), их обработку с помощью алгоритмов машинного обучения, в том числе сверточных нейросетей, и создание предсказательных моделей. Эмпирическая апробация методологии на выборке из 120 полей в различных агроклиматических условиях продемонстрировала повышение точности прогнозирования урожайности на 15–20% по сравнению с традиционными подходами. Выявлены перспективные направления оптимизации систем поддержки принятия решений в точном земледелии на основе анализа больших данных. Полученные результаты имеют значимость для развития устойчивого сельского хозяйства и повышения глобальной продовольственной безопасности.

The article is dedicated to analyzing the potential applications of big data and neural network technologies in precision agriculture to increase crop yields and the sustainability of agricultural production. Based on a review of current research, key trends and gaps in this area have been identified. An original methodology has been proposed, which includes the collection and integration of diverse data sets (remote sensing data, sensor data, agrochemical soil indicators, etc.), their processing using machine learning algorithms, including convolutional neural networks, and the creation of predictive models. Empirical testing of the methodology on a sample of 120 fields in various agroclimatic conditions demonstrated an increase in yield prediction accuracy by 15–20% compared to traditional approaches. Promising directions for optimizing decision-support systems in precision agriculture based on big data analysis have been identified. The results obtained are significant for the development of sustainable agriculture and enhancing global food security.

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