Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach
Abstract
:1. Introduction
2. Results
2.1. Correlations between WSI
2.2. Water Potential
2.3. Indicators of Crop Water Status Based on Canopy Temperature
2.3.1. Normalised Canopy or Leaf Temperature with Reference to Air Temperature
2.3.2. Crop Water Stress Index (CWSI)
2.4. Stomatal Conductance
2.5. Models
3. Discussion
4. Materials and Methods
4.1. Plant Material and Experimental Site
4.2. Meteorological Monitoring and Assessment
4.3. Grapevine Water Status
4.4. Stomatal Conductance
4.5. In Situ Thermal Imagery
4.6. Statistical Analysis
4.7. Machine Learning Methodology
4.7.1. Dataset Creation
- Plant description (grape variety, irrigation treatment);
- Meteorological information (relative humidity, air temperature);
- Thermal images;
- records;
- records.
4.7.2. Models
The idea of ensemble learning is to build a prediction model by combining the strengths of a collection of simpler base models. (…) Ensemble learning can be broken down into two tasks: developing a population of base learners from the training data and then combining them to form the composite predictor.
4.7.3. Evaluation Methodology
- 1.
- Model ranking;
- 2.
- Hyperparameter optimisation;
- 3.
- Final model evaluation.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Hyperparameter | Search Space | Optimal Value |
---|---|---|
n_estimators | [50, …, 1000] | 585 |
min_samples_split | [2, …, 133] | 6 |
min_samples_leaf | [1, …, 133] | 6 |
loss | [squared_error, absolute_error, quantile] | squared_error |
learning_rate | [1 × 10−5, …, 100] | 0.020596 |
subsample | [0.2, …, 1] | 0.4 |
max_depth | [1, …, 1000] | 75 |
Hyperparameter | Search Space | Optimal Value |
---|---|---|
n_estimators | [50, …, 1000] | 528 |
min_samples_split | [2, …, 133] | 2 |
min_samples_leaf | [1, …, 133] | 1 |
bootstrap | [False, True] | False |
criterion | [squared_error, friedman_mse] | squared_error |
Hyperparameter | Search Space | Optimal Value |
---|---|---|
n_estimators | [50, …, 1000] | 806 |
min_samples_split | [2, …, 133] | 2 |
min_samples_leaf | [1, …, 133] | 1 |
bootstrap | [False, True] | True |
criterion | [squared_error, friedman_mse] | squared_error |
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Rank | Model | Module | MAE (MPa) | |
---|---|---|---|---|
1 | GradientBoostingRegressor | Ensemble | 0.761 | 0.097 |
2 | ExtraTreesRegressor | Ensemble | 0.756 | 0.101 |
3 | RandomForestRegressor | Ensemble | 0.709 | 0.107 |
6 | SVR | SVM | 0.687 | 0.113 |
7 | NuSVR | SVM | 0.676 | 0.118 |
9 | LassoLarsIC | Linear | 0.599 | 0.132 |
10 | LinearRegression | Linear | 0.596 | 0.132 |
25 | DecisionTreeRegressor | Tree | 0.548 | 0.130 |
29 | MLPRegressor | Neural Network | 0.490 | 0.140 |
31 | KNeighborsRegressor | Neighbors | 0.445 | 0.155 |
36 | GaussianProcessRegressor | Gaussian Process | 0.218 | 0.146 |
42 | RadiusNeighborsRegressor | Neighbors | −0.029 | 0.213 |
Model | Validation | Test | ||
---|---|---|---|---|
MAE (MPa) | MAE (MPa) | |||
GradientBoostingRegressor | 0.786 | 0.093 | 0.830 | 0.073 |
ExtraTreesRegressor | 0.759 | 0.100 | 0.833 | 0.072 |
RandomForestRegressor | 0.710 | 0.107 | 0.798 | 0.079 |
Reference | No. of Varieties | Target | Predictors | Model | Platform | |
---|---|---|---|---|---|---|
Our approach | 5 | Thermal imaging, gs, meteorology | ExtraTrees | Handheld | 0.83 | |
[96] | 2 | Hyperspectral bands | Algorithm based on the search for covariance | Handheld | 0.97 | |
[84] | 1 | (MD) | Hyperspectral bands | Artificial Neural Network | UAV | 0.87 |
[90] | 6 | (MD) | NIR spectrometer | Rotation Forest, M5 trees | Handheld | 0.84 |
[97] | 1 | (MD) | CWSI | Linear Regression | UAV, Field sensors | 0.83 |
[92] | 1 | (MD) | NIR spectrometer | Partial Least Squares | ATV | 0.69 |
[98] | 1 | (MD) | CWSI | Linear Regression | UAV | 0.51 |
[123] | 2 | Thermal indices | Two-way Analysis of Variance (ANOVA) | UAV, Handheld | 0.50 |
Source | Variable |
---|---|
Porometer | Leaf temperature |
Thermal Camera | Temperature canopy east Temperature canopy west |
Manually Recorded | Time of day (MM/MD) Variety |
Meteorological Station | VPD Relative humidity Air temperature |
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Damásio, M.; Barbosa, M.; Deus, J.; Fernandes, E.; Leitão, A.; Albino, L.; Fonseca, F.; Silvestre, J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants 2023, 12, 4142. https://doi.org/10.3390/plants12244142
Damásio M, Barbosa M, Deus J, Fernandes E, Leitão A, Albino L, Fonseca F, Silvestre J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. Plants. 2023; 12(24):4142. https://doi.org/10.3390/plants12244142
Chicago/Turabian StyleDamásio, Miguel, Miguel Barbosa, João Deus, Eduardo Fernandes, André Leitão, Luís Albino, Filipe Fonseca, and José Silvestre. 2023. "Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach" Plants 12, no. 24: 4142. https://doi.org/10.3390/plants12244142