Intelligent Decision Support System for Optimizing Irrigation and Minimizing Operational Costs in Agribusiness

Анотація

Increasing pressure on water resources, rising energy costs, and the need to improve the economic efficiency of agricultural production determine the relevance of developing intelligent decision support systems for optimizing irrigation and minimizing operational costs in agribusiness. The aim of the article is to analyze theoretical and methodological approaches to the formation and application of intelligent systems that support managerial decision making in irrigation management under conditions of uncertainty and resource constraints.

 The research employed methods of scientific literature analysis to examine current scholarly developments in the selected topic, as well as methods of generalization and systematization to present the research findings. The study reveals that irrigation optimization should be considered a complex economic and technological process aimed at achieving a balance between resource consumption and production outcomes. The structure of operational costs in agricultural enterprises is analyzed, and the main factors influencing their formation are identified, including energy intensity, technological level, and environmental conditions. It is established that the use of intelligent decision support systems enables more efficient allocation of water and energy resources, reducing operational costs while maintaining or increasing crop yields. It is noted that such systems are based on a multi-level architecture that integrates data collection, processing, and analysis modules, ensuring the transformation of heterogeneous data into actionable recommendations. It is found that the application of machine learning and optimization algorithms increases the accuracy and adaptability of irrigation planning. It is substantiated that the effectiveness of system implementation should be evaluated using a combination of economic, operational, and environmental indicators.

The conclusions state that intelligent systems have significant potential to enhance the resilience and competitiveness of agribusiness, although their successful implementation depends on technological readiness, data availability, and organizational adaptation.