## 4 Discussion  
The early selection of relevant plant phenotypes is key to the advance of plant breeding under a rapidly changing environment. We induced drought stress in _A. thaliana_ plants by withholding watering for 7 days (S1, moderate stress) and 12 days (S2, severe stress), leading to differences in plants' aspect (Figure 2a) and SWC (46.10 \(\pm\) 3.29% and 33.17 \(\pm\) 2.50%, respectively) (Figure 2b). To classify _A. thaliana_ drought stress severity we developed three machine learning models using leaf reflectance spectra. Even though a substantial overlap of leaf reflectance spectra was observed (Figure 2), all classifiers produced very high evaluation scores (F1 > 90%) and we assessed their relative performance through stochastic variation. When passing a thousand random seeds to each model's split function (which separates the train and test data) and the classifier's internal random number generator, all the scores showed a tendency to decrease. As expected, DT showed the most pronounced drop, owing to the algorithm's unstable response to small variations in the training set [35], while RF and XGB appeared to be more robust to this kind of random fluctuation as their prediction scores remained above 90% (Table 2), with RF displaying higher medians and less variance (Figure 3). It may be surprising that XGB, being known for its extreme ability to fit the data, did not surpass RF. However, XGB is also known for its tendency to overfit, which has to be counteracted by a careful setting of its parameters. For difficult data, it is  
Figure 7: Histograms representing the most used features for each machine learning algorithm. normally worth using XGBoost, but for easy data (such as ours apparently is, considering the quality of the achieved results) there is no advantage, and a more straightforward ensemble such as random forest tends to generalize better. The models were able to adequately distinguish between unstressed and water stressed plants, however, unstressed plants were more often incorrectly classified as moderately stressed by DT. Discriminating between plants exposed to moderate and severe stress should be considered a harder task for the classifier given that although statistically different, these groups contain a slight overlap in their SWC levels (at around 39% SWC) (Figure 2b). However, moderately stressed plants were incorrectly classified as unstressed plants most of the time in all algorithms, in spite of severely stressed plants being indeed much more mistaken with moderately stressed ones (Figure 4). An important factor to consider in these predictions is the biological aging of the plants, which may introduce confounding effects related with pigment changes during senescence, which will affect leaf reflectance values [36]. In fact, unstressed plants in our experimental setup include individuals with different DAS (21, 28, and 33), which may explain the moderate stress misclassification as unstressed. By virtue of its simplicity, DT was able to use the fewest number of features, and it was no surprise that the ensemble techniques (RF and XGB) were able to explore more features within the visible spectrum (Figure 7). From the initial high dimension feature space, we observe that all algorithms based their predictions on three main wavelength ranges within the reflectance spectrum, falling within the violet (400-425 nm), green (525-570 nm), and red (650 nm) regions of visible light. In addition, RF also considered a blue-green transition at around 475-500 nm. This narrowing of the initial feature space is useful in the development of future predictors as it will potentially contribute to a more reliable classification with a lesser chance of overfitting, given that noisy information will no longer be input to the model. We also found that reflectance values at wavelengths around 670 nm were particularly important, as they were observed to have very high discriminative power in all three algorithms (Figure 6).