## 3 Results
### Daylight Samples Subset  
The GBT-based classifiers modeled on the set enclosing only the samples acquired during daylight showed increased efficiency of about 10%, for distinguishing the stressed-from the normal-state samples.  
_Original set of features._ The model was built with 100 trees, 4 as maximal depth and 0.05 as the learning rate. The classification accuracy reached 80.0% \(\pm\) 2.2%. The obtained prediction of each sample forming the corresponding test set is illustrated in Figure 5. The most dominant feature for the discrimination, with a relative importance weight of 35.2%, was Hjorth complexity calculated using 5 min windows. The importance of the following four features was considerably lower: GHE at 30 min (8.9%), Hjorth mobility  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**All Samples**} \\ \hline \multicolumn{2}{c}{**Original Set of Features**} & \multicolumn{2}{c}{**Reduced Set of Features**} \\ \hline
**Feature** & **Weight** & **Feature** & **Weight** \\ \hline IQR 2 min & 19.4\% & IQR 2 min & 15.5\% \\ Purple noise 30 min & 6.4\% & GHE 30 min & 8.2\% \\ GHE 30 min & 6.3\% & Purple noise 30 min & 7.5\% \\ Hjorth complexity 2 min & 4.2\% & Hjorth complexity 2 min & 5.6\% \\ Hjorth complexity 10 & 3.3\% & Hjorth mobility 5 min & 4.6\% \\ min & \multicolumn{2}{c}{**Daylight Samples**} \\ \hline \multicolumn{4}{c}{**Original Set of Features**} & \multicolumn{2}{c}{**Reduced Set of Features**} \\ \hline
**Feature** & **Weight** & **Feature** & **Weight** \\ \hline Hjorth complexity 5 min & 35.2\% & Hjorth complexity 5 min & 33.4\% \\ GHE 30 min & 8.9\% & GHE 30 min & 10.9\% \\ Hjorth mobility 10 min & 6.3\% & Hjorth mobility 10 min & 6.6\% \\ Purple noise 30 min & 5.7\% & Margin factor 15 s & 5.8\% \\ Blue noise 30 min & 4.1\% & White noise 30 min & 4.6\% \\ \hline \multicolumn{4}{c}{**Night Samples**} \\ \hline
**Original Set of Features** & \multicolumn{2}{c}{**Reduced Set of Features**} \\ \hline
**Feature** & **Weight** & **Feature** & **Weight** \\ \hline Logarithmic wentropy & 36.1\% & Logarithmic wentropy & 37.2\% \\
15 s & & 15 s & \\ Impulse factor 15 s & 8.0\% & Crest factor 15 s & 11.0\% \\ Mean of WD4 15 s & 6.5\% & Frequency center 15 s & 5.2\% \\ Frequency center 15 s & 2.9\% & Minimum 15 s & 4.6\% \\ Shape factor 30 min & 2.7\% & Hjorth complexity 30 s & 4.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of the five most dominant features used by the built GBT-based models to identify plant status for each of the studied scenarios.  
at 10 min (6.3%), and purple (5.7%) and white noise (4.1%), both calculated at 30 min. The least dominant features were the mean of the wavelet decomposition of order 8 at 30 min and 10 min, the kurtosis at 30 min, the brown noise at 10 min and the maximum of the wavelet decomposition of order 8 at 10 min (Table S1).  
Reduced set of featuresThe reduced set included 122 features in total. Moreover, the optimal GBT parameters remained unchanged from those obtained with the original set of features. The model showed an accuracy of 79.9% \(\pm\) 1.3% (Figure S3) and relied again principally on the Hjorth complexity at 5 min for the discrimination (relative weight of importance: 33.4%). GHE calculated for windows of 30 min remained the second important feature with approximately three times smaller weight of importance (10.9%). It was followed by the Hjorth mobility at 10 min (6.6%), margin factor at 15 s (5.8%) and the white noise at 30 min (4.6%). On the opposite side the kurtosis at 30 min, the brown noise at 2 and 5 min, the RMS frequency at 5 min and the shape factor at 5 min were the features with the smallest contribution for the discrimination (Table S1).