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Modeling GABA-mediated stress in pomegranate using integrated structural equation modeling and Machine Learning
Saeedeh Zarbakhsh1, Ali Reza Shahsavar2, Nazim S Gruda3
1College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China; Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran.
Exogenous application of γ-aminobutyric acid (GABA) mitigates combined drought and salinity stress in pomegranate. This study integrates Structural Equation Modeling (SEM) with machine learning (ML) to understand crop stress responses and enhance pomegranate tolerance.
Area of Science:
- Plant Physiology and Stress Biology
- Agricultural Science
- Biochemistry
Background:
- Concurrent abiotic stresses like drought and salinity pose significant threats to global crop production.
- These stresses induce complex physiological disruptions that are difficult to model using conventional statistical methods.
- Pomegranate (Punica granatum L.) is susceptible to abiotic stresses, impacting its yield and quality.
Purpose of the Study:
- To investigate the efficacy of exogenous γ-aminobutyric acid (GABA) in mitigating combined drought and salinity stress in pomegranate.
- To employ an integrative analytical approach combining Structural Equation Modeling (SEM) with machine learning (ML) to understand stress responses.
- To enhance the interpretability and predictive power of stress response models in crops.
Main Methods:
- Pomegranate plants were subjected to combined drought and salinity stress for 45 days.
- Exogenous application of 40 mM γ-aminobutyric acid (GABA) was tested as a mitigation strategy.
- Structural Equation Modeling (SEM) was integrated with machine learning methods, including Random Forest (RF), Support Vector Regression (SVR), and SHapley Additive exPlanations (SHAP), for analysis.
Main Results:
- Combined stress severely impaired photosynthetic efficiency (Fv/Fm), pigments, and stomatal traits, causing oxidative damage.
- GABA application effectively counteracted these negative effects, preserving photosynthetic integrity and promoting secondary metabolite accumulation (phenolics, flavonoids, anthocyanins).
- SEM-ML models showed high predictive accuracy (R² up to 0.980) for biochemical traits, pigments, and PSII efficiency, with SHAP highlighting combined stress and GABA as key predictors.
Conclusions:
- The integration of SEM with ML provides a robust and interpretable framework for analyzing complex plant stress responses.
- Exogenous GABA application is a promising strategy for enhancing pomegranate tolerance to combined drought and salinity stress.
- This integrative approach offers a generalizable methodology for studying and improving crop resilience to abiotic stresses.
