Exploring Drought Response: Machine-Learning-Based Classification of Rice Tolerance Using Root and Physiological Traits
Abstract
1. Introduction
2. Materials and Methods
2.1. Plant Material
2.2. Experimental Design
2.3. Physiological Measurements
2.3.1. Proline Content
2.3.2. Chlorophyll Content
2.3.3. Malondialdehyde (MDA) Content
2.3.4. Relative Water Content (RWC)
2.4. Root Anatomical Measurements
2.4.1. Cortex Area
2.4.2. Endodermis Characteristics
2.4.3. Exodermis Characteristics
2.4.4. Metaxylem Vessel Dimensions
2.5. Data Summary and Descriptive Statistics
2.6. Machine Learning and AI Implementation
2.6.1. Data Overview and Target Variable
2.6.2. Random Forest Model
2.6.3. Neural Network Model
2.6.4. Stacking Ensemble Model
2.6.5. Reproducibility and Computational Environment
2.6.6. Model Performance Evaluation and Statistical Comparison
3. Results
3.1. Trait Variability and Physiological Correlations Under Contrasting Water Regimes
3.2. Random Forest Analysis
3.3. Neural Network Model Analysis
3.4. Stacking Ensemble Model Analysis
3.5. Model Comparison and Statistical Validation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
AUC | Area Under the Curve |
Chla | Chlorophyll a |
Chlb | Chlorophyll b |
DL | Deep Learning |
FW | Fresh Weight |
H2O2 | Hydrogen Peroxide |
MDA | Malondialdehyde |
ML | Machine Learning |
MLP | Multi-Layer Perceptron |
PEG | Polyethylene Glycol |
RF | Random Forest |
RWC | Relative Water Content |
SHAP | Shapley Additive Explanations |
SMOTE | Synthetic Minority Over-sampling Technique |
SVM | Support Vector Machine |
TW | Turgid Weight |
DW | Dry Weight |
ROC | Receiver Operating Characteristic |
TCA | Trichloroacetic Acid |
TBA | Thiobarbituric Acid |
FAA | Formalin-Acetic Acid-Alcohol |
Appendix A
Cultivar | Drought Tolerant Class | Type |
---|---|---|
Kab yang | tolerant | Landrace |
Kam | tolerant | Landrace |
Khao soi | moderate | Landrace |
Luang bunma | moderate | Landrace |
Luang kaeo | susceptible | Landrace |
Hang nak | tolerant | Landrace |
Hom thung | moderate | Landrace |
Long ma | moderate | Landrace |
Samart | susceptible | Landrace |
Sam ruang | moderate | Landrace |
Mae phueng | moderate | Landrace |
Mayom | moderate | Landrace |
Mafai | moderate | Landrace |
Maprang | moderate | Landrace |
Dor yuan | susceptible | Landrace |
Jaew daeng | moderate | Landrace |
Khithom dam | susceptible | Landrace |
Phra in | tolerant | Landrace |
I-daeng noi | moderate | Landrace |
I-khiao non thung | tolerant | Landrace |
CT9993 | tolerant | Tolerant check (CT9993) |
KDMLl105 | moderate | Susceptible check (KDML105) |
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Model | Rationale and Role | Unique Contribution and Core Algorithmic Summary |
---|---|---|
Random Forest (RF) | Handles high-dimensional, mixed-type data; interpretable feature ranking | Establishes baseline accuracy and trait importance; uses bootstrap aggregation, Gini impurity minimization, and feature importance via mean decrease in Gini. |
Multi-Layer Perceptron (MLP) | Captures complex, nonlinear relationships | Detects subtle interactions among traits; fully connected layers with LeakyReLU activation and softmax output, optimized by Adam and categorical cross-entropy loss. |
Stacking Ensemble | Integrates diverse classifiers for improved accuracy and robustness | Maximizes performance by combining RF, SVM, XGBoost, and MLP base models, with XGBoost as the meta-learner to determine the final classification. |
Metric | 5 Folds Cross Validation (Mean ± SD) | Best Single Split | ||
---|---|---|---|---|
Random Forest | MLP | Stacking Ensemble | (Model/Value) | |
Accuracy | 0.546 ± 0.067 | 0.485 ± 0.080 | 0.433 ± 0.100 | Stacking Ensemble/0.818 |
Balanced Accuracy | 0.469 ± 0.054 | 0.389 ± 0.066 | 0.320 ± 0.090 | Stacking Ensemble/0.821 |
Precision (macro) | 0.494 ± 0.052 | 0.403 ± 0.121 | 0.306 ± 0.109 | Stacking Ensemble/0.830 |
Recall (macro) | 0.469 ± 0.054 | 0.389 ± 0.066 | 0.320 ± 0.090 | Stacking Ensemble/0.813 |
F1 Score (macro) | 0.459 ± 0.058 | 0.380 ± 0.069 | 0.304 ± 0.090 | Stacking Ensemble/0.812 |
ROC AUC (micro/ovo) | 0.730 ± 0.033 | 0.702 ± 0.066 | 0.440 ± 0.066 | Random Forest/0.973 |
Log Loss † | 0.946 ± 0.032 | 2.188 ± 0.561 | 1.704 ± 0.269 | MLP/0.555 (lowest) |
Matthews Corrcoef | 0.235 ± 0.073 | 0.086 ± 0.120 | −0.033 ± 0.162 | Stacking Ensemble/0.738 |
Cohen’s Kappa | 0.222 ± 0.081 | 0.079 ± 0.120 | −0.028 ± 0.155 | Stacking Ensemble/0.728 |
Hamming Loss † | 0.454 ± 0.077 | 0.515 ± 0.081 | 0.567 ± 0.093 | Stacking Ensemble/0.182 (lowest) |
Metric | RF VS. MLP | RF VS. Stacking Ensemble | MLP VS. Stacking Ensemble |
---|---|---|---|
Accuracy | 0.098 | 0.063 | 0.273 |
Balanced Accuracy | 0.074 | 0.017 * | 0.273 |
Precision (macro) | 0.120 | 0.007 * | 0.138 |
Recall (macro) | 0.074 | 0.017 * | 0.273 |
F1 Score (macro) | 0.137 | 0.007 * | 0.206 |
ROC AUC (ovo, micro) | 0.512 | 0.002 * | 0.002 * |
Log Loss † | 0.007 * | 0.006 * | 0.045 * |
Matthews Corrcoef | 0.063 | 0.021 * | 0.241 |
Cohen’s Kappa | 0.083 | 0.021 * | 0.214 |
Hamming Loss † | 0.109 | 0.063 | 0.214 |
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Gunnula, W.; Kanawapee, N.; Chokthaweepanich, H.; Phansak, P. Exploring Drought Response: Machine-Learning-Based Classification of Rice Tolerance Using Root and Physiological Traits. Agronomy 2025, 15, 1840. https://doi.org/10.3390/agronomy15081840
Gunnula W, Kanawapee N, Chokthaweepanich H, Phansak P. Exploring Drought Response: Machine-Learning-Based Classification of Rice Tolerance Using Root and Physiological Traits. Agronomy. 2025; 15(8):1840. https://doi.org/10.3390/agronomy15081840
Chicago/Turabian StyleGunnula, Wuttichai, Nantawan Kanawapee, Hathairat Chokthaweepanich, and Piyaporn Phansak. 2025. "Exploring Drought Response: Machine-Learning-Based Classification of Rice Tolerance Using Root and Physiological Traits" Agronomy 15, no. 8: 1840. https://doi.org/10.3390/agronomy15081840
APA StyleGunnula, W., Kanawapee, N., Chokthaweepanich, H., & Phansak, P. (2025). Exploring Drought Response: Machine-Learning-Based Classification of Rice Tolerance Using Root and Physiological Traits. Agronomy, 15(8), 1840. https://doi.org/10.3390/agronomy15081840