Biological mechanism-based intelligent predictive control method for light environment optimization in vertical plant factories for Oryza Sativa seedling

In vertical plant factories, dynamic light management tailored to crop developmental stages is essential yet unattainable through conventional fixed-lighting approaches. This study presents an integrated control framework termed NMPC-PAR-LSTM, which synergizes three functional modules: a Michaelis-Menten kinetics model that generates growth-stage-specific photosynthetic targets, an LSTM network that forecasts environmental variations over a 20-step-ahead horizon, and an NMPC optimizer incorporating physiological constraints. Comparative evaluation demonstrates that the NMPC-PAR-LSTM system achieves a cumulative tracking error of 136 μmol·m −2 ·s −1 , representing reductions of 91.6% relative to Linear MPC (1610.6), 90.6% relative to LSTM-MPC (1451), and 45.2% relative to MPC-PAR-LSTM (248). The framework also exhibits superior control stability, with an input variation rate of only 4.2%, compared to 26.0% for Linear MPC and 88.3% for LSTM-MPC. By aligning supplemental lighting with the physiological demands of crops, this approach provides a viable pathway toward sustainable vertical agriculture.