Adaptive Irrigation Models for Improving Water Use Efficiency in Greenhouse Production

Abstract

Greenhouse irrigation management in soilless production systems faces pressure to reduce water and fertilizer consumption while maintaining commercially viable yields. Most documented irrigation approaches rely on one or two measured variables to trigger events, leaving the remaining sensor data unused in the control decision. Purpose. To describe the Smart-Flow Matrix (SFM), an adaptive irrigation management framework that integrates five operational data streams within a unified decision engine, and to present resource-efficiency and yield metrics from its commercial deployment. Method. SFM was deployed in a commercial hydroponic strawberry operation (coconut coir substrate, drip fertigation, greenhouse area up to approximately 10 hectares) in Kyiv Oblast, Ukraine. SFM coordinates volumetric water content, pore-water electrical conductivity, cumulative photosynthetically active radiation, air temperature, and drainage analytics through coordinated threshold logic with seasonal parameter adjustment across three growth stages. Operational records from the SFM period were compared with records from the preceding timer-based management period in the same facility. Results. Water and fertilizer consumption decreased by approximately 45 to 50 percent relative to the timer-based baseline. Yield reached 1.7 to 1.87 kilograms per plant under commercial conditions. Drainage fraction converged to target ranges within three to four irrigation cycles following each parameter adjustment. Conclusion. SFM demonstrates that coordinated multi-parameter irrigation logic with seasonal adaptation can achieve measurable resource savings at commercial greenhouse scale without compromising yield stability.