Intelligent Digital Management Systems in Agricultural Production

Анотація

Introduction. The article substantiates the need for implementing intelligent digital systems (IDS) in the operations of domestic agricultural enterprises. Such systems integrate sensor networks (IoT), big data analytics, artificial intelligence, and cloud services to monitor and optimise production processes. It is emphasised that IDS contribute to higher operational efficiency and competitiveness of agricultural enterprises through rapid and informed management decisions, enhanced adaptability, and increased resilience under high risk and uncertainty. It is demonstrated that the use of IDS in agricultural production marks a transition toward the concept of Agriculture 4.0, which is grounded in digital technologies, automation, and big data analytics aimed at optimising the entire production cycle. Purpose. The purpose of this article is to generalise the key features of IDS implementation in agricultural enterprises and to identify vectors of their influence on the transformation of operational and strategic production management, as well as their contribution to improving production efficiency and achieving economic, environmental, and social sustainability. Methodology. To conduct the research and address the outlined tasks, the following methods were applied: abstract–logical and monographic methods were used for theoretical generalisation and formulating conclusions; the system analysis method was used to determine the structural elements of IDS; deduction and induction supported the identification of negative factors affecting IDS implementation in the agricultural sector and the justification of measures to mitigate them. Results. The study demonstrates that IDS transform management approaches within agricultural enterprises and confirm the shift toward the new paradigm of Agriculture 4.0. It is concluded that IDS enable a transition from reactive management–based on experience and intuition–to proactive management founded on real-time data analytics. The effectiveness of IDS is ensured by their core technologies: IoT establishes the sensor infrastructure for data collection; AI and ML enable data analysis and knowledge generation; cloud and edge computing provide the necessary computational environment; and management systems (FMS/ERP) visualise information and supply decision-support tools. The advantages of IDS implementation in crop production, livestock farming, and the management of agricultural machinery and technical assets are substantiated. It is proven that IDS serve as a tool for enhancing efficiency, resilience, and competitiveness of agricultural enterprises, requiring not only technological investment but also the development of human capital and digital infrastructure.

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