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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. [ABSTRACT FROM AUTHOR]

• Gaussian Mixture Model (GMM) was used to compensate the photosynthetic rate model. • Multi-objective model of greenhouse light after error compensation was built. • An improved NSGA-II algorithm was proposed to solve the optimization model. • Results showed the effectiveness of the proposed optimization model and algorithm. Research on the optimization and control of greenhouse light environment is of great significance to improve the production efficiency and economic benefits of greenhouse crop. The optimization of greenhouse light environment should not only meet the requirements of crop photosynthesis, but also reduce the energy consumption cost during light supplement. Therefore, in this paper, a multi-objective optimization model of greenhouse light environment was first established, which was aimed at maximizing the photosynthetic rate of crop and minimizing the energy consumption cost. Then, there were errors between the outputs of photosynthetic rate model and the actual values, which led to that the optimization results based on the photosynthetic rate model were not the actual optimal values, so Gaussian mixture model (GMM) was used to describe the error characteristics of photosynthetic rate model. The error compensation of photosynthetic rate model was realized, and it was introduced into the optimization objective, thus forming a multi-objective optimization model of greenhouse light environment after error compensation. In addition, an improved NSGA-II algorithm based on average distance clustering (ADCNSGA-II) was proposed to solve the multi-objective optimization model. The algorithm divided the whole population into several small populations by using average distance, and then selected, crossed, and mutated small populations. This operation could effectively maintain the diversity of Pareto optimal solution set, and further improve the convergence of algorithm. Finally, taking tomato in a solar greenhouse of the experimental base of Shenyang Agricultural University in Northeast China as the research crop, the established model and the proposed optimal regulation method were verified through simulation experiments. The results showed that the RMSE and MAPE of photosynthetic rate model based on error compensation were 0.7641 and 2.6413 respectively, and the CC of the model was 0.9803, indicating that the model has good prediction performance. Moreover, ADCNSGA-II algorithm was used to solve the multi-objective optimization model after error compensation, and the results were compared with those obtained by solving the optimization model before error compensation. The optimization results obtained by solving the optimization model after error compensation were closer to the actual values, which further proved the reliability of the proposed optimal regulation method. [ABSTRACT FROM AUTHOR]

• A hierarchical optimization control method of greenhouse light environment is proposed. • The method ensures optimality and feasibility of light saturation point using crop growth model. • It can realize hierarchical decision-making of light saturation point and fill light operation. • The results show the effectiveness of the proposed optimization control method. It is of great significance to study the optimization control of greenhouse light environment that can meet the demands of crop growth and reduce the regulation cost to improve the production efficiency and economic benefits of greenhouse crops. However, due to the complexity of the actual optimization control problem of greenhouse light environment, such as multi-objective, high dimensionality and numerous constraints, the optimization control of greenhouse light environment is still a challenging research topic. Therefore, a hierarchical optimization control method based on crop growth model is proposed, which divides the optimization control problem of light environment into two levels, namely optimal-level and control-level. This method simplifies the computational complexity of the original optimization control problem of light environment by means of hierarchical structure, and thus realizes the solution of the optimization control problem of light environment. Meanwhile, the multi-objective optimization control of light environment is realized through the hierarchical decision-making for the control target of light environment and the operation of light supplement. Finally, taking tomato in solar greenhouse as the research object, the simulation results show that the method is effective. In addition, the average regulation cost of this method is 0.3084$, which is lower than 0.3677 $ of the optimization control method of light environment with fixed threshold as target, it further proves the energy-saving effect of the proposed optimization control method. [ABSTRACT FROM AUTHOR]