## 3 Results  
The novel methodology for feature extraction uses substantially less computing time when compared to the previously proposed approach [14]. More precisely, the previous approach took around 4 h and 30 min to process the signal of 5 days, while the new one did the computation within 30 min which is nine times faster. The results below all use the novel methodology. The highest classification accuracy was obtained for models build upon features extracted from windows with length of 1 min (Table 1). For 15 windows, the related accuracy reached 80.20%. As the length of the window increases, the accuracy is slightly reduced. Nevertheless, a more important drop in the performance is observed for shorter windows i.e., \(l\) = 30 s.  
The precision, recall and specificity are, again, highest for windows of 1 min. Moreover, only in that case, the recall and specificity are relatively close in values, which indicates that the models built on features from 1-min windows are able to equally identify the normal and the stressed state. For all other tested window lengths there is a relatively large difference between the recall and the specificity. For instance, the models related to windows of 5 min have a higher specificity than recall value and therefore are more prone to correctly detect the normal state than the stressed. The opposite can be observed for the windows of 2 min, where the recall values are higher than the specificity of the models. In the same context, although with a poorer performance, the models built on windows of 30 s show similar behavior as those related to 2 min. In summary, these findings (shown in more details in Table 1) suggest that among the analyzed windows lengths, 1 min is the optimal one. Hence, a length of 1 min will be used for the further analyses. Table 2 shows, for each window length, the most discriminative features among the first 10 ranked by the XGB gain measure. One should note that this list of the 10 most discriminative features includes the same feature several times but calculated within different windows of the taken signal segment i.e., different columns of the matrix resulting from the feature extraction procedure. In general, the Hjorth complexity is the feature that is highly ranked for all of the tested scenarios. Additionally, it appears as the most important for the discrimination performed by the models built on features from windows with lengths of 1 to 5 min. Subsequent to the Hjorth complexity, shape and crest factors also appear as dominant features for the classification. The effect on number of windows on the classification performance was tested for all the samples calculated within 24 h for \(l\) = 1 min and \(N\) = 30. More precisely, each plant was represented by 2822 samples, where one half corresponded to the normal state and the other half to the stressed state. The obtained results revealed that in general, the model performance is tending to improve with the increase of the number of windows. As shown in Figure 4, all four measures, accuracy, precision, recall, and specificity calculated for the stressed state taken as a positive class, mainly follow an upward trend. However, the recall distribution is more dispersed than other three distributions, indicating that for different \(N\) i.e., different number of features, there is a relatively high difference in correctly predicting the stressed state. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Window Length _l_** & **Number of Windows _N_** & **Classification Accuracy** & **Precision \({}^{*}\) & **Recall (Sensitivity) \({}^{*}\)** & **Specificity \({}^{*}\)** \\ \hline \multirow{3}{*}{**30 s**} & 5 & 56.66\% & 56.44\% & 58.33\% & 54.99\% \\  & 10 & 58.15\% & 57.01\% & 66.30\% & 50.00\% \\  & 15 & 56.81\% & 56.06\% & 63.02\% & 50.61\% \\ \hline \multirow{3}{  
Considering the most discriminative features, Hjorth complexity, crest factor and the similarity with the brown noise appeared as most dominant ones regarding the ranking provided by the XGB gain measure.