## 4 Discussion  
The present study proposes a novel approach for identifying, with an accuracy of above 80%, a stressed state in a tomato plant growing in typical production environment through the use of local signal information from the plant's electrophysiological response. More precisely, by combining electrophysiological data related to different abiotic and biotic stimuli, the proposed method offers a classifier that detects the stress commonly encountered in soilless tomato cultivation, which is referred to, in this study, as general stress. The studied stimuli are drought, nutrient deficit and infestation with spider mites. Compared to a recent state-of-the-art methodology for classifying the plant electrical response [14], another advantage that the proposed workflow introduces is the decrease of the computing time required for extraction of the signal information used for the classification. Although both approaches rely on the same signal features, the newly  
Figure 4: Classification performance when increasing the number of windows with length of 1 min. Each blue point represents the exact calculated value for the related \(N\), whereas the line in magenta portrays the line fitted to the related distributions in a least-squares sense. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features’ Groups** & \
\begin{tabular}{c} **Classification** \\ **Accuracy** \\ \end{tabular} & **Precision 1 ** &
\begin{tabular}{c} **Recall** \\ **(Sensitivity) 1 ** \\ \end{tabular} & **Specificity 1 ** \\ \hline
**1** & 82.56\% & 82.46\% & 82.71\% & 82.41\% \\
**1** + **5** & 82.05\% & 82.01\% & 82.11\% & 81.99\% \\
**2** + **5** + **6** & 81.91\% & 87.34\% & 74.64\% & 89.18\% \\
**1** + **2** + **3** + **5** & 82.97\% & 82.48\% & 83.73\% & 82.21\% \\
**2** + **3** + **4** + **5** + **6** & 84.51\% & 80.76\% & 90.61\% & 78.41\% \\
**1** + **2** + **3** + **4** + **5** + **6** & 78.78\% & 78.24\% & 79.73\% & 77.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance summary of the models with the highest accuracy among all the tested combinations for different numbers of features’ groups. The features are calculated for \(N=15\) windows of length \(l=1\) min. proposed one, as opposed to the previous approach, includes in the extraction process the storage and reuse of the calculation from the preceding steps. Hence, when applying both of them in similar settings, the new approach performs the features extraction tasks approximately nine times faster, which strengthen its potential to be used in everyday practice. The lack of reported observations regarding the computation time required by the other related approaches in the current literature limited the feasibility to extend this comparison along the relevant state-of-the-art. By keeping the features from the previous steps in a given sample, the new methodology allows the deliberate inclusion of historical data in the discrimination of signal patterns related to each state. The extent of the history depends on the chosen number of windows, which is also directly related to the dimension of the feature space used for building the model. The approach for extracting the signal features allows the selection not only of the extent of the feature space but also of the length of the signal portion at which the information will be extracted. The findings presented here reveal that signal intervals of 1 min contain the most pertinent information for discriminating general stress in tomato plants caused by common stressors in soilless cultivation. By halving this interval, the discrimination power is considerably diminished, whereas for extended lengths of several minutes, the discriminative information is still present, if somewhat reduced. This observation is in line with the findings presented for identification of stress in tomato plants related predominantly to presence of spider mites, which revealed that the windows of few minutes encode information for identifying the state of the plant [14].