Abstract
Green houses play a vital role in modern agriculture. Artificial light illumination is very important in a green house. While light is necessary for plant growth, excessive light in a green house may not bring more profit and even damages plants. Developing a plant-physiology-based light control strategy in a green house is important, which implies that a state-space model on photosynthetic activities is very useful because modern control theories and techniques are usually developed according to model structures in the state space. In this work, a simplified model structure on photosystem II activities was developed with seven state variables and chlorophyll fluorescence (ChlF) as the observable variable. Experiments on ChlF were performed. The Levenberg-Marquardt algorithm was used to estimate model parameters from experimental data. The model structure can fit experimental data with a small relative error (<2%). ChlF under different light intensities were simulated to show the effect of light intensity on ChlF emission. A simplified model structure with fewer state variables and model parameters will be more robust to perturbations and model parameter estimation. The model structure is thus expected useful in future green-house light control strategy development.
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Claims 3
Claim · model-written summary
The seven-state photosystem II model fitted experimental chlorophyll fluorescence with relative error below 2%.
Not reportedTaxon
Verbatim
e0“a simplified model structure on photosystem II activities was developed with seven state variables and chlorophyll fluorescence (ChlF) as the observable variable”
e1“The model structure can fit experimental data with a small relative error (<2%)”
Claim · model-written summary
Simulated chlorophyll fluorescence varied under different light intensities.
Not reportedTaxonDirection
Verbatim
e0“ChlF under different light intensities were simulated to show the effect of light intensity on ChlF emission”
Claim · model-written summary
The authors expect fewer state variables and parameters to improve model robustness to perturbations and parameter estimation.
- Outcome
- more robust to perturbations and model parameter estimatione0
- Comparator
- fewer state variables and model parameterse0
Not reportedTaxon
Verbatim
e0“A simplified model structure with fewer state variables and model parameters will be more robust to perturbations and model parameter estimation”