Adaptive critic design and Hopfield neural network based simulation of time delayed photosynthetic production and prey–predator model

Construction of a photosynthetic rate prediction model for greenhouse strawberries with distributed regulation of light environment

In winter and spring, for greenhouses with larger areas and stereoscopic cultivation, distributed light environment regulation based on photosynthetic rate prediction model can better ensure good crop growth. In this paper, strawberries at flowering-fruit stage were used as the test crop, and the LI-6800 portable photosynthesis system was used to control the leaf chamber environment and obtain sample data by nested photosynthetic rate combination experiments under temperature, light and CO2 concentration conditions to study the photosynthetic rate prediction model construction method. For a small-sample, nonlinear real experimental data set validated by grey relational analysis, a photosynthetic rate prediction model was developed based on Support vector regression (SVR), and the particle swarm algorithm (PSO) was used to search the influence of the empirical values of parameters, such as the penalty parameter C, accuracy ε and kernel constant g, on the model prediction performance. The modeling and prediction results show that the PSO-SVR method outperforms the commonly used algorithms such as MLR, BP, SVR and RF in terms of prediction performance and generalization on a small sample data set. The research in this paper achieves accurate prediction of photosynthetic rate of strawberry and lays the foundation for subsequent distributed regulation of greenhouse strawberry light environment.

A photosynthetic rate prediction model using improved RBF neural network

A photosynthetic prediction rate model is a theoretical basis for light environmental regulation, and the existing photosynthetic rate prediction models are limited by low modeling speed and prediction accuracy. Therefore, this paper analyses effects of light quality on photosynthesis rate, and proposes a method based on Radial basis function (RBF) optimized by Quantum genetic algorithm (QGA) to establish photosynthetic rate prediction model. We selected "golden embryo² formula 98-1F1" cucumber seedlings as experimental material and used LI-6800 to record the photosynthetic rates under different temperatures, light intensities and light quality. Experimental data is used to train and test the proposed model. The determinant coefficient of the model between the predicted and the measured values is 0.996, the straight slope of linear fitting is 1.000, and the straight intercept of linear fitting is 0.061. Moreover, the proposed method is compared with 6 artificial intelligence algorithms. The comparison results also validate that the proposed model has the highest accuracy compared with other algorithms.