ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Photosynthesis and Photobiology
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1590884
This article is part of the Research TopicPhotosynthesis under Variable Environmental ConditionsView all 5 articles
Plant photosynthesis in Basil (C3) and Maize (C4) under different light conditions as basis of an AIbased model for PAM fluorescence / gasexchange correlation
Provisionally accepted
- Darmstadt University of Technology, Darmstadt, Germany
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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.
Keywords: Chlorophyll Fluorescence, gas exchange, machine learning, Photosynthesis prediction, C3/C4 plants
Received: 10 Mar 2025; Accepted: 21 Apr 2025.
Copyright: © 2025 Pappert, Klir, Jokic, Ühlein, Tran Quoc and Kaldenhoff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Isabell Pappert, Darmstadt University of Technology, Darmstadt, Germany
Stefan Klir, Darmstadt University of Technology, Darmstadt, Germany
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