Knowledge-based control systems for commercial single stem rose production

Systems analysis was performed to develop knowledge-based control systems (KBS) for single stem rose production. Systems components and control strategies for the KBS were identified from the analysis. The control strategy for daytime temperatures was derived by balancing the conflicting goals of rose production and costs of environmental modification. Nighttime temperature control strategy was determined through qualitative reasoning of the relationship between the plant's photosynthetic efficiency and respiratory energy metabolism. A fuzzy inference paradigm was applied to facilitate the systems design. Two fuzzy inference systems (FIS) were designed to determine the daytime and nighttime greenhouse set point temperatures. A set of experiments was conducted to obtain the information on dimensional measurements of single stem rose (Rosa hybrida L., cv. Lady Diana) and the corresponding stem dry weight. An adaptive neuro-fuzzy inference system (ANFIS) was devised that predicted the dry weight for single stem roses from simple non-destructive measurements. An on-line computational algorithm for greenhouse heating and ventilation costs was developed according to the daily energy consumption of each component of heating and ventilation equipment. Two sets of role bases were derived for daytime and nighttime temperature settings.