Intelligent decision support systems in precision agriculture and resource conservation

Abstract

Ukrainian agriculture faces productivity pressures amid wartime disruptions and volatile commodity markets. Traditional agriculture enterprise management no longer suffices for maintaining competitiveness. Intelligent agromonitoring systems integrate remote sensing and ground sensors to support evidence-based decision-making.

Problem statement. Fragmented adoption of intelligent technologies limits effectiveness. Many agriculture enterprises lack integrated platforms combining satellite imagery with soil sensor data. Small producers face barriers including unreliable rural connectivity, high equipment costs, and insufficient technical expertise.

Unresolved aspects. Key challenges include limited interoperability between monitoring hardware and software, absence of standardized data formats linking field observations with accounting, and insufficient local-language interfaces. The effectiveness of cooperative purchasing models requires further investigation.

Purpose of the article. To substantiate the strategic role of the intelligent systems based on analytical support of the agromonitoring data and to propose a framework for integrating remote sensing and predictive models into farm planning.

Presentation of the main material. The article analyzes four intelligent monitoring technologies: satellite remote sensing, weather stations, soil sensors, and drones. It examines decision support across operational levels including irrigation timing and pest control, tactical levels covering crop selection, and strategic levels such as land investment. Resource optimization for fertilizers, water, and fuel demonstrates cost reductions of fifteen to thirty percent. Implementation barriers including connectivity gaps and skill shortages are addressed with corresponding solutions. Special attention is given to early warning for disease outbreaks and drought stress detection.

Conclusions. the intelligent systems based on analytical support of the agromonitoring data enhances decision quality, reduces input expenditures, and improves yield forecasts. A comprehensive monitoring framework enables enterprises to navigate production uncertainty and sustain competitiveness.