Data-Driven Modeling of Photosynthesis Regulation Under Oscillating Light Condition - Part I: In-Silico Exploration

TL;DR

This study uses data-driven system identification techniques to develop simplified, control-oriented models of photosynthesis regulation under oscillating light, enabling better understanding and potential control of plant responses.

Contribution

It introduces a novel approach combining in-silico data, BLA, and LPV modeling to characterize photosynthesis regulation under dynamic light conditions.

Findings

Successful estimation of second-order LTI models across conditions

Development of a LPV model with light intensity as scheduling parameter

Demonstration of in-silico modeling for photosynthesis regulation

Abstract

This paper explores the application of data-driven system identification techniques in the frequency domain to obtain simplified, control-oriented models of photosynthesis regulation under oscillating light conditions. In-silico datasets are generated using simulations of the physics-based Basic DREAM Model (BDM) Funete et al.[2024], with light intensity signals -- comprising DC (static) and AC (modulated) components as input and chlorophyll fluorescence (ChlF) as output. Using these data, the Best Linear Approximation (BLA) method is employed to estimate second-order linear time-invariant (LTI) transfer function models across different operating conditions defined by DC levels and modulation frequencies of light intensity. Building on these local models, a Linear Parameter-Varying (LPV) representation is constructed, in which the scheduling parameter is defined by the DC values of the light intensity, providing a compact state-space representation of the system dynamics.