At the heart of the controller is a microcontroller unit (MCU) that runs preset algorithms. Basic systems follow fixed schedules or simple if‑then rules – for example, "if temperature > 28°C, reduce light intensity by 20%." More advanced platforms leverage artificial intelligence to perform dynamic optimization.
These AI systems monitor plant physiological parameters such as leaf temperature, chlorophyll fluorescence, or even stem diameter growth. The algorithm then explores different light "recipes" (intensity, spectrum, photoperiod) and learns which combination maximizes biomass or secondary metabolite content. In a trial for medical cannabis cultivation, an AI‑controlled system improved cannabinoid yield by 18% compared to a fixed schedule, while simultaneously reducing electricity consumption by 12%.
