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Purpose <p>Cross-instrument comparability of O-J-I-P (OJIP) chlorophyll fluorescence transients remains limited because blue and red excitation strategies generate fluorescence curves with different spectral responses and temporal morphology. This study aimed to develop a transfer learning framework for standardizing heterogeneous OJIP data and improving cotton salt stress diagnosis.</p> Methods <p>OJIP measurements from 14,145 cotton leaf samples collected in greenhouse and field experiments were used. FluorPen-FP110 and Pocket PEA data were compared under blue and red excitation. A phase-specific OJIP-Standard Normal Variate (OJIP-SNV) preprocessing method was developed and evaluated with support vector machine (SVM), bidirectional long short-term memory (Bi-LSTM), one-dimensional convolutional neural network (1D-CNN), and Cotton Salt Stress-OJIP-Net (CSS-OJIP-Net). A conditional generative adversarial network framework, FluoToFluo, combined OJIP-SNV preprocessing with a Bi-LSTM transform for cross-instrument fluorescence migration.</p> Results <p>Blue- and red-excitation measurements showed clear differences in fluorescence intensity distributions, with weak correlations for several intensity parameters but stronger agreement for relative fluorescence parameters. OJIP-SNV improved SVM and Bi-LSTM classification performance. CSS-OJIP-Net achieved 87.80% accuracy and 84.70% recall on Pocket PEA data. After FluoToFluo (OJIP-SNV) transfer and sample-search augmentation, accuracy and recall increased to 90.29% and 90.97%, respectively.</p> Conclusion <p>OJIP-SNV and FluoToFluo reduced instrument-dependent fluorescence discrepancies and provided a practical route for multi-instrument OJIP databases and precision diagnosis of cotton salt stress.</p>

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Constructing a Cross-Instrument Transfer Learning Method for Chlorophyll a Fluorescence OJIP Transient Using OJIP-SNV and CGAN: A Case Study for Cotton Salt Stress Diagnosis

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Jiayi Li,
  • Nan Liu,
  • Huijun Zhou,
  • Haiyan Zeng,
  • Wei Yang,
  • Haiyong Weng,
  • Beibei Zhou,
  • Hong Sun,
  • Minzan Li

摘要

Purpose

Cross-instrument comparability of O-J-I-P (OJIP) chlorophyll fluorescence transients remains limited because blue and red excitation strategies generate fluorescence curves with different spectral responses and temporal morphology. This study aimed to develop a transfer learning framework for standardizing heterogeneous OJIP data and improving cotton salt stress diagnosis.

Methods

OJIP measurements from 14,145 cotton leaf samples collected in greenhouse and field experiments were used. FluorPen-FP110 and Pocket PEA data were compared under blue and red excitation. A phase-specific OJIP-Standard Normal Variate (OJIP-SNV) preprocessing method was developed and evaluated with support vector machine (SVM), bidirectional long short-term memory (Bi-LSTM), one-dimensional convolutional neural network (1D-CNN), and Cotton Salt Stress-OJIP-Net (CSS-OJIP-Net). A conditional generative adversarial network framework, FluoToFluo, combined OJIP-SNV preprocessing with a Bi-LSTM transform for cross-instrument fluorescence migration.

Results

Blue- and red-excitation measurements showed clear differences in fluorescence intensity distributions, with weak correlations for several intensity parameters but stronger agreement for relative fluorescence parameters. OJIP-SNV improved SVM and Bi-LSTM classification performance. CSS-OJIP-Net achieved 87.80% accuracy and 84.70% recall on Pocket PEA data. After FluoToFluo (OJIP-SNV) transfer and sample-search augmentation, accuracy and recall increased to 90.29% and 90.97%, respectively.

Conclusion

OJIP-SNV and FluoToFluo reduced instrument-dependent fluorescence discrepancies and provided a practical route for multi-instrument OJIP databases and precision diagnosis of cotton salt stress.