An optimization approach for environmental control using quantum genetic algorithm and support vector regression

Photosynthesis serves as the foundation for vegetable crop yield. It is crucial to appropriately regulate the environmental parameters associated with photosynthesis to ensure efficient production and energy conservation in plant factories or greenhouses. In this research, we proposed a novel optimization approach for determining the target value of environmental control, aiming to balance plant growth and energy cost. By employing hydroponic lettuces as experimental samples, we measured their photosynthetic rates (Pn) under various combinations of four environmental factors: air temperature (AT), nutrient solution temperature (NST), photon flux density (PFD), and CO2 concentration ([CO2]). The photosynthetic data were combined with the support vector regression algorithm to develop a Pn prediction model. This model achieved a coefficient of determination of 0.9748, a root mean square error value of 0.9302 µmol∙m−2∙s−1, and a mean absolute error value of 1.1813 µmol∙m−2∙s−1. The model provide data for subsequent environmental control. The quantum genetic algorithm (QGA) was employed to search the optimal Pn and corresponding PFD, [CO2], and NST at different ATs. The fitness function for QGA was developed considering both the Pn and the energy consumption. This approach could calculate the target environments (PFD, [CO2], and NST) for any given AT. Compared with the Pn maximization approach, the energy cost-saving rate was 1.5 to 3.5 times higher than the Pn loss. The proposed approach could quickly and accurately determine an optimal environmental control target value, outperforming other approaches in complexity and generality. Thus, this study offers an elegant approach to environmental control for hydroponic cultivation.