## 3 Results
### Night Samples Subset  
The models built on a set of samples recorded only during the night showed inferior results compared to the previous two scenarios. Original set of featuresThe GBT model was built with 200 trees of maximum depth of 4 and 0.05 as learning rate. It classified the samples with an accuracy of 65.8% \(\pm\) 0.6% (Figure S4) relying mainly on features calculated within windows of 15 s. The most discriminative appeared to be logarithmic wentropy at 15 s (36.1%), followed by impulse factor (8.0%), mean of the wavelet decomposition of order 4 (6.5%) and the frequency center (2.9%). The fifth in order was the shape factor (2.7%) calculated at 30 min. The mean of the wavelet decomposition of order 8 at 5, 10 and 30 min, together with the blue noise at 10 min and the brown noise at 5 min, were last in the order of contribution (Table S1). Reduced set of featuresRegarding this sample subset, 111 features in total formed the reduced subset of features. The GBT model built employing these features used 100 numbers of trees with a maximum depth of 4 and 0.1 as learning rate and performed  
Figure 5: Predicted class of each sample forming the test set used to evaluate the model obtained when analyzing the daylight samples subset with the reduced set of features. The color of the samples indicates the class that has been attributed to them by the classification model—blue represents the normal, whereas red is the stressed state. For each plant, the plotted samples overlay the normalized feature mean value of the signal calculated for a window of 5 min. with an accuracy of 65.4% \(\pm\) 1.3% (Figure S5). The logarithmic wentropy calculated at 15 s appeared again as the most discriminative feature with relative importance weight of 37.2%. The following four were also from small window sizes: crest factor at 15 s (11.0%), frequency center at 15 s (5.2%), minimum at 15 s (4.6%) and the Hjorth complexity at 30 s (4.3%). The brown noise at 10 min, together with the white noise at 30 s and 1 min, the kurtosis at 1 min and the minimum of the wavelet decomposition of order 1 at 1 min, were the features with the smallest contribution (Table S1). The results describing the performance of each classifier are summarized in Table 3. The models based on signals acquired during daylight presented the highest accuracy as well as high values for the remaining performance measures regarding the prediction of the stressed states: precision, recall and specificity. On the other hand, even though the night samples models performed with high precision and specificity, its recall value, representing the sensitivity of the model, was about 36-38%; therefore, these models were very limited in predicting the stressed state (refer to Figures S4 and S5 in the Supplementary Material). Overall, from the summary given in Table 2, one could observe that the Hjorth complexity at 5 min, IQR at 2 min and GHE at 30 min appeared as the most discriminative features. The logarithmic wentropy at 15 s appeared as an important feature for the discrimination of the plant status only when the night samples were used. Figure 6 shows the distributions without outliers of these four features for each sample subset and each window length. Hjorth complexity presented differences in distribution between the normal and the stressed state for all three sample sets and all window lengths. However, the distinction between these distributions was most notable for daylight samples, mainly for window lengths between 1 and 30 min. In the case of night samples, the Hjorth complexity distributions were notably closer. Similar findings as the Hjorth complexity feature could also be observed from the distributions of IQR. Considering GHE, distribution dissimilarities were mainly observed for the samples acquired during daylight, with most notable distinction for windows of 30 min. In line with the obtained modeling results, the logarithmic wentropy portrayed notable differences in distribution mainly for the set of night samples.