MANAGING THE SAFETY OF REGIONAL AGRICULTURAL PRODUCTION IN HIGH-RISK ENVIRONMENTS USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE METHODS

DOI 10.32651/263-79

Issue № 3, 2026, article № 9, pages 79-91

Section:

Language: Russian

Original language title: УПРАВЛЕНИЕ БЕЗОПАСНОСТЬЮ РЕГИОНАЛЬНОГО СЕЛЬСКОХОЗЯЙСТВЕННОГО ПРОИЗВОДСТВА В УСЛОВИЯХ ВЫСОКИХ РИСКОВ С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ И ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Keywords: SAFETY, FOOD SECURITY, THREATS, RISKS, DIGITAL ECONOMY, MANAGEMENT, MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, AGRICULTURE, AGRICULTURAL PRODUCTION

Abstract: The paper considers the goals and objectives of security management in conditions of high threats and risks in the formation of a digital economy carried out by the state at the level of the regional agro-industrial complex, including agricultural production, where the main internal and external factors influencing the improvement and development of the industry were considered and summarized, and a SWOT analysis matrix using artificial intelligence was formulated. and machine learning. Food safety has been developed on the basis of a probabilistic assessment of the implementation of organizational, organizational, resource, and technical and technological processes using a universal methodology. The methods and technologies of applying machine learning in the tasks of ensuring and managing security based on data from the information space of the digital economy are presented. The methodology and methods of assessing and forecasting threats and risks, assessing the vulnerability of food and related economic systems, as well as choosing the optimal strategy to counter them are proposed. Algorithms and the most suitable programming languages for the implementation of solutions to the problem are selected. It is noted that all this is possible only if there is an information space organized in accordance with the paradigm of the digital economy.

Authors: Semkin Aleksandr Grigorevich, Voronin Evgenii Alekseevich