Greenhouse light and CO2 regulation considering cost and photosynthesis rate using i-nsGA Ⅱ

Natural resources, such as light and CO2, are required to increase crop yields in protected agriculture. Developing efficient regulation methods to reduce costs and increase yields has become a major challenge for actual production. This work proposed a framework for meeting this challenge and supporting the management of environmental regulation. Photosynthesis rate was used as the yield index, and a photosynthesis rate model was constructed using support vector regression. Moreover, a regulation cost function for light and CO2 was constructed. Then, the non-inferior solutions (NIS) for environmental regulation were determined by an improved non-dominated sorting genetic algorithm Ⅱ (i-nsGA Ⅱ). The curvature theory was applied to calculate the optimal solution in the NIS set. A set of seedling tomato data was used to demonstrate the usefulness of the proposed method. Compared with the other two regulation methods (saturation regulation method and U-chord method), the results indicated that the proposed method had the best performance. About 80% of the maximum photosynthesis rate could be obtained at a regulation cost of about 51%. This research presents a new strategy for optimizing the greenhouse environment, which can be applied as a knowledge-informed expert system to manage environmental regulation.