Double Hyperbolic Curve Model for Plant Light Response Fitting
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Solution Overview
Problem
Existing regression models for determining plant light environment-carbon sequestration benefit curves, such as the rectangular hyperbola, non-rectangular hyperbola, single exponential, double exponential, modified rectangular hyperbolic, and non-rectangular hyperbola models, exhibit relatively low fitting accuracy compared to the double hyperbolic curve model, resulting in significant differences between fitted and experimental values.
Innovation Solution
A regression-based plant light environment-carbon sequestration benefit curve determination method using a double hyperbolic curve model is implemented, which involves data regression analysis using various models, verification of regression equations based on root mean squared error (RMSE) values, and selection of the double hyperbolic curve model for optimal fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional regression models (rectangular hyperbola, non-rectangular hyperbola, single exponential, double exponential, modified rectangular hyperbolic) are used to determine plant light environment-carbon sequestration benefit curves, then the modeling process is simple and well-established, but the fitting accuracy is low resulting in significant differences between fitted and experimental values
Solution Approach 1:
The patent changes the mathematical form of the regression model from traditional single-hyperbola or exponential forms to a double hyperbolic curve model. This parameter change in the model structure enables significantly improved fitting accuracy (RMSE reduced from 0.70-0.43 to 0.25) while maintaining the regression-based analytical approach, thus resolving the contradiction between fitting accuracy and model complexity
Solution Approach 2:
The patent combines multiple hyperbolic curve components into a composite double hyperbolic model. By integrating two hyperbolic functions with different parameters (α1, β1, α2, β2, γ), the model captures complex plant photosynthetic responses to light environments more accurately than single-component models, achieving superior fitting performance without excessive complexity
2Measurement precision
If the double hyperbolic curve model is used to determine plant light environment-carbon sequestration benefit curves, then the fitting accuracy is significantly improved with lower RMSE values, but the model structure becomes more complex compared to traditional models
Solution Approach 1:
The patent introduces additional parameters (α2, β2, γ) to transform the traditional single-hyperbola model into a double hyperbolic model. This parameter expansion enables the model to capture nuanced variations in plant photosynthetic responses across different light environments, achieving RMSE of 0.25 versus 0.70-0.43 for traditional models, thus prioritizing measurement precision while accepting controlled increases in model complexity
Data Source
AI summary
The present disclosure belongs to the technical field of plant photoresponse and discloses a regression-based plant light environment-carbon sequestration benefit (a light response curve) determination method and system, and a medium. The method includes: performing data regression analysis by using various models; verifying data regression equations corresponding to the respective models; selecting a double hyperbolic curve regression model as an optimal one of the models; measuring red-blue light source by using a Li6400XT photosynthesis system; obtaining a light response curve through regression of the double hyperbolic curve; and obtaining corresponding formulas of light compensation (LCP) and a light saturation point (LSP) through regression. According to the present disclosure, a new double hyperbolic curve regression model is first adopted to fit the light response curve, so as to construct a more accurate photoresponse model.