Greenhouse lighting is vital for plant growth and contributes to nearly 30% of operational costs. However, managing lighting in response to dynamic sunlight conditions and varying photosynthetic photon flux density (PPFD) requirements across crop types remains a major challenge, which results in excessive energy use. This paper presents a digital twin (DT) adaptive control framework for greenhouse lighting, leveraging quantum sensors and reinforcement learning (RL) to enable energy-efficient, multi-zone operation. The proposed system dynamically adjusts multi light-emitting diode (LED) intensities in the extended Photosynthetically Active Radiation spectrum to satisfy uniform PPFD thresholds (single-crop scenario) or differentiated PPFD thresholds (multi-crop scenario). A set of 12 adaptive control strategies was evaluated, employing Genetic Algorithm (GA) and RL optimizers to configure proportional-integral-derivative (PID) controller parameters as well as their PI and P subsets. Real world validation demonstrates that the RL-based PI control with shared coefficients (RL-PI (Eq)) delivers the most robust performance across scenarios, achieving an average mean error of 1.248 µmol s−1 m−2 with a standard deviation of 10.661 µmol s−1 m−2. Compared to a baseline on-off controller, the proposed strategy reduces electrical energy consumption by 23.6% and energy-related costs by 23.2%, while maintaining precise PPFD regulation. These findings highlight the potential of DT adaptive control systems to advance sustainable, cost-effective, and scalable multi crop greenhouse lighting management.

DOI: https://doi.org/10.1016/j.atech.2026.101984