Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation

Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation

Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO2 fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under vary...

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Bibliographic Details
Main Authors: Isabell Pappert, Stefan Klir, Luca Jokic, Celine Ühlein, Khanh Tran Quoc, Ralf Kaldenhoff
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Plant Science
Subjects:
chlorophyll fluorescence
gas exchange
machine learning
photosynthesis prediction
C3/C4 plants
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2025.1590884/full
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Summary:Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO2 fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. Despite their physiological link, data from both methods do not always correlate. To systematically investigate this relationship, photosynthetic parameters were measured in maize (Zea mays, C4) and basil (Ocimum basilicum, C3) under different photon densities and spectral compositions. Maize showed the highest CO2 assimilation rate of 30.99 ± 1.54 µmol CO2/(m²s) under 2000 PAR green light (527 nm), while basil reached 10.56 ± 0.92 µmol CO2/(m²s) under red light (630 nm). PAM-derived electron transport rates (ETR) increased with light intensity in a pattern similar to CO2 assimilation, but did not reliably reflect its absolute values under all conditions. To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. It achieved superior performance (R² = 0.847; MSE = 5.24) compared to the Random Forest model. Our model enables accurate photosynthesis prediction from PAM data across light intensities and spectral conditions in both C3 and C4 plants.
ISSN:1664-462X

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