Optimal Regulation Method of Greenhouse Light Environment considering Photosynthetic Rate Model Error

Qingyun Yuan, Tan Liu, Xiaoming Ding, Yonggang Wang, Yingli Cao and Francesco Lolli

Mathematical Problems in Engineering, 2022, vol. 2022, 1-14

Abstract: Due to the complexity of photosynthesis of greenhouse crops, there are inevitable errors between the established photosynthetic rate model and the actual value, which makes it difficult to guarantee the optimality and even feasibility of the optimal regulation of greenhouse light environment based on the original photosynthetic rate model. To solve these problems, this paper proposes an optimal regulation method of greenhouse light environment considering the error of photosynthetic rate model. The main contributions of this method are as follows: firstly, the photosynthetic rate model is established by using the error compensation extreme learning machine method to reduce the error between the model output and the actual value. Then, the model output is introduced into the optimal regulation problem of greenhouse light environment to form the optimal regulation model of greenhouse light environment after error compensation, and the constrained adaptive particle swarm optimization algorithm is used to solve the optimal regulation model, which ensures the rapid convergence of optimization results, makes the optimization results closer to the actual optimal values, and improves the reliability of optimal regulation of greenhouse light environment. Finally, the effectiveness of the proposed optimal regulation method is verified by experiments. The simulation results also show that the proposed optimal regulation method can give a reasonable light intensity setting value in time under different temperature, humidity, and CO2 conditions inside greenhouse, meet the needs of photosynthesis of actual greenhouse crops, and make the growth of greenhouse crops efficient and stable.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2108334

DOI: 10.1155/2022/2108334

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