## 2 Materials and Methods
### Preprocessing  
Several preprocessing steps were applied to the raw electrophysiological recording. _Notch filtering._ To eliminate eventual noise generated by the electrical power source and its harmonics, two band-stop filters at 50 Hz and 100 Hz were applied on the signal. Windowing and feature extraction. To extract local signal features, the recordings were divided into relatively small windows. Following a recently proposed approach [14], in each of the isolated windows, 34 features characterizing the signal information in either time or frequency domain were calculated using Matlab (version R2020a, The MathWorks Inc., Natick, Massachusetts, USA). These features could be separated in six different groups:  
* _Group 1_: minimum, maximum, variance, skewness, kurtosis, and interquartile range (IQR) of the windowed signal
* _Group 2_: Hjorth mobility, Hjorth complexity, Generalized Hurst exponent (GHE), Shannon wavelet package entropy (wentropy), logarithmic wentropy and the root mean square (RMS) of the windowed signal
* _Group 3_: impulse, margin, shape and crest factor
* _Group 4_: frequency center, frequency RMS and the root variance of the frequency
* _Group 5_: similarity between the signal noise and the color noises: white, blue, brown, pink, and purple, respectively  
Figure 2: Typical representative recorded signal from three plants under normal and stress state showing a nychthemeral rhythm (the gray blocs indicate the night period). These signals are included in the dataset where each of them is representing a different type of stressor. * _Group 6_: e minimum, maximum and mean value of the wavelet decomposition (WD) of order 8 at levels 1, 4 and 8, respectively The initial step of the windowing approach involves choosing a signal interval (or segment) ending at time-point t\({}_{0}\). This segment is further divided in \(N\) windows of same length \(l\) (Figure 3). In other words, the length of the segment is \(N\)-\(l\). After calculating the chosen features in each window, all of the extracted 34-\(N\) features extracted within the initial segment will represent the first sample of the data to model i.e., the sample at time point t\({}_{0}\) (Figure 3A). In the second step the initial segment is shifted forward by a length \(l\) (Figure 3B). Equivalently to the previous step, all the 34-\(N\) features extracted within this shifted segment will represent the second sample of the data for modeling i.e., the sample at time point t\({}_{1}\). One could note that the features for the first \(N\)-1 windows were already calculated in the  
Figure 3: Schematic representation of the developed approach for feature extraction. **Panel A:** The chosen segment, ending at time point t\({}_{0}\), is divided in \(N\) windows of length \(l\). In each of these windows 34 different features are calculated (Features_i: t\({}_{0}\), i = 1,..., \(N\)) and all of the 34-\(N\) extracted features are used to define the sample at time point t\({}_{0}\). **Panel B:** The segment chosen initially is shifted for a length l, also shifting its ending to the time point t\({}_{1}\). As the first \(N\)-1 features at this step represent the last \(N\)-1 features from the previous step (Features_i: t\({}_{0}\), i = 2,..., \(N\)) they have already been calculated. Hence, the only remaining features to compute at this step are those within the last, \(N\)th, window (Features_N: t\({}_{1}\)). All of the 34-\(N\) extracted features extracted at this step now define the sample of the data for modeling at time point t\({}_{1}\). previous step and consequently, at this step, only the features in the last window should be computed. Once the second sample is defined, the segment is shifted again for the same length \(l\) and the next sample is determined in similar manner as in the previous steps. This procedure is repeated until the end of the chosen recordings, resulting in a matrix of \(r\) rows representing the data samples and \(c\) columns corresponding to the integrality of the extracted features vectors.