Journal of Scientific Research and Reports

Floriculture combines high product value with unusually strict requirements for visual quality, uniformity, flowering time and postharvest performance. These characteristics make controlled-environment flower production particularly suitable for precision management, but they also expose limitations in technologies developed primarily for food crops. This critical narrative review evaluates how sensors, controlled-environment technologies and automation are being integrated into precision floriculture, with emphasis on cut flowers and potted ornamentals produced in greenhouses or other controlled environments. Literature was selected through transparent searching of accessible scholarly sources, critical appraisal and citation chaining, with recent evidence prioritised while retaining technically important earlier work. The evidence indicates that substrate-moisture sensing and automated irrigation represent the most mature precision interventions in ornamental production, with commercial and experimental studies demonstrating improved control of water application and, in some settings, gains in crop uniformity, labour efficiency and profitability. Wireless environmental sensing is technically well established, yet sensor placement, calibration, communication reliability and the conversion of measurements into crop-specific thresholds remain persistent constraints. Light-emitting diode systems provide fine control of daily light integral, photoperiod and spectrum, but ornamental responses are strongly genotype- and stage-dependent, and energy consequences depend on climate, glazing and control strategy. Computer vision has advanced rapidly for rose monitoring, flower grading and sorting, while robotic harvesting has progressed from laboratory demonstrations to commercial-greenhouse prototypes. By contrast, digital twins, reinforcement learning and highly autonomous climate control remain supported mainly by broader greenhouse-horticulture evidence rather than floriculture-specific validation. Sustainability therefore cannot be inferred from automation alone: water and nutrient savings may be offset by electricity demand, embodied infrastructure, data systems or poorly chosen control targets. The review identifies a need for multi-season commercial trials, interoperable data standards, plant-centred sensing, integrated water-energy-quality optimisation and life-cycle assessment that treats marketable flower quality as a primary system outcome.