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

文献类型： 外文期刊

作者： Gao, Pan 1 ; Lu, Miao 1 ; Yang, Yongxia 1 ; Wu, Huarui 3 ; Mao, Hanping 4 ; Hu, Jin 1 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Jiangsu Univ, Minist Educ, Key Lab Modern Agr Equipment & Technol, Zhenjiang 212013, Peoples R China

关键词： Multi-objective optimization; Photosynthesis rate; Curvature theory; Environmental regulation; Regulation cost

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：8.5； 五年影响因子：8.3 ）

ISSN： 0957-4174

年卷期： 2024 年 237 卷

页码：

收录情况： SCI

摘要： 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.

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