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

Xinyan Chen; Zhaohui Jiang; Qile Tai; Chunshan Shen; Yuan Rao; Wu Zhang; Xinyan Chen; Zhaohui Jiang; Qile Tai; Chunshan Shen; Yuan Rao; Wu Zhang

doi:10.3934/mbe.2022596

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12774-12791. doi: 10.3934/mbe.2022596

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Research article

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

School of Information and Computer Science, Anhui Agricultural University, Hefei, 230036, China

Received: 14 April 2022 Revised: 01 August 2022 Accepted: 16 August 2022 Published: 01 September 2022

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.
- greenhouse,
- light environment,
- distributed regulation,
- photosynthetic rate model,
- PSO-SVR,
- strawberry
Citation: Xinyan Chen, Zhaohui Jiang, Qile Tai, Chunshan Shen, Yuan Rao, Wu Zhang. Construction of a photosynthetic rate prediction model for greenhouse strawberries with distributed regulation of light environment[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12774-12791. doi: 10.3934/mbe.2022596

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Abstract

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.

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