## 3 Results  
The visual appearance of well-watered controls (S0) and progressively drought stressed plants by withholding watering for 7 days (S1) and 12 days (S2) is shown in Figure 2a. Moderate changes were visible at S1 and much more pronounced changes at S2. Well-watered pots (S0) presented SWC \(\sim\)100%, reaching 46.10 \(\pm\) 3.29% soil water content after 7 days (S1) and 33.17 \(\pm\) 2.50% after 12 days (S2) of water stress (Figure 2b). Significant differences in leaf pigment composition were found between S0 and S2 (Figure 2c). Water stress for 12 days (S2) decreased significantly the content of Chl.a, from 30.57 \(\pm\) 2.07 to 23.01 \(\pm\) 2.11 \(\mu\)g cm\({}^{-2}\) and Chl.b, from 5.38 \(\pm\) 0.41 to 4.34 \(\pm\) 0.49 \(\mu\)g cm\({}^{-2}\) (\(p\)! 0.05). as well as the ratio between Chl.a and Chl.b (from 5.69 \(\pm\) 0.12 to 5.33 \(\pm\) 0.15) and between Chl.a+b and C.x+c (from 4.99 \(\pm\) 0.13 and 4.22 \(\pm\) 0.12) (\(p\)! 0.05). The absorption spectra of photosynthetic pigments and anthocyanins are shown in Figure 2d. Total photosynthetic pigments show a peak in the blue region of the spectrum (\(\sim\)430-460 nm) and a peak on the red region of the spectrum (650-670 nm). A shoulder is observed centered at the wavelength of 470 nm. Anthocyanins show a significant absorption band between 400 and 450 nm. A significant overlap is visible in Figure 2e, showing leaf reflectance spectra measured in S0, S1 and S2 plants. To establish machine learning classifiers with high predictive capability of water stress in _A. thaliana_, models from three algorithms (DT, RF, and XGB) were developed with hyperparameter search and tuning based on stratified 10-fold cross-validation. The best parameter combinations found for each classifier on the development data sets are listed in Table 1.  
Table 2 shows the predictive performance of each estimator as a result of the randomly generated independent runs, each using different train/test data splits and classifier seeds. Figure 3 illustrates the dispersion of these results in a boxplot. As evaluation metrics, we have used both training and test accuracy, and also the balanced test accuracy and the test F1-score. Balanced accuracy represents the raw accuracy where each sample is weighted according to the inverse prevalence of its true class. Figure 2: (**a**) Representative images of _A. thaliana_ ecotype Columbia (Col-0) plants under control (S0), moderate stress (S1) and severe stress (S2); (**b**) soil water content at S0, S1 and S2 conditions (mean \(\pm\) S.D. ; asterisks represent significant differences (\(p\) < 0.05)); (**c**) leaf pigment composition per leaf area (chlorophyll a (Chl.a), chlorophyll b (Chl.b), Chl.a/Chl.b, total carotenoids (Cx+c) and Chl.a+b/Cx+c (box plots show median values and interquartile range (IQR), asterisks represent significant differences (\(p\) < 0.05)); (**d**) in vitro absorbance spectra of total photosynthetic pigments and anthocyanins; (**e**) in vivo plant reflectance curves at each wavelength of the spectra at S0, S1, S2. All algorithms were able to induce models that achieve high average scores, but the ensemble methods performed better than single trees. DT attained not only the lowest average scores, but also the highest variance (balanced test accuracy 0.88 \(\pm\) 0.12, test F1 0.93 \(\pm\) 0.06), while RF and XGB attained balanced test accuracies of 0.94 \(\pm\) 0.09 and 0.92 \(\pm\) 0.10, respectively, and test F1-score of 0.96 \(\pm\) 0.05 for both. The lower dispersion of RF and XGB results suggests that these methods have a higher robustness to random selections of features, samples and splits of the data. The boxplot reveals that, in terms of median, RF is the clear winner. Overall, RF reached the best performance with the lowest variance, proving to be a good choice for the present data set.