## 4 Discussion  
By employing machine learning techniques for data analysis, the presented exploratory study demonstrates the presence of changes in the electrophysiology signal acquired from commercial tomato plants growing in a typical production environment, caused by infestation with spider mites. It presents a methodology based on local signal features and a GBT algorithm, for modeling a classifier that is able to distinguish, with an accuracy of around 80%, the normal from the stressed plant state where the stress is induced by the infestation. Furthermore, the study shows that models built on samples recorded during the day outperform the classifiers developed using samples, including whole 24 h cycles and, even more significantly, those based only on dark period recordings. The simultaneous high recall, precision and specificity values demonstrate the high potential of the daylight-based classifiers to distinguish between the normal and the stressed state of an infested tomato plant. In contrast, models built on the night sample sets show high precision and specificity but, at the same time, low sensitivity and therefore are more prone to predict only the normal state correctly. It is insightful that signals collected during daylight provided a more accurate classification of plant states than either combined or nighttime-only samples. Such findings are potentially related to plant metabolism being higher during the day with water/nutrient  
Figure 6: Distribution of the four features observed as the most discriminative for the GBT classification. Each of them is represented without outliers for all the three explored sets of samples and all window lengths. sorption, enhanced sap flow and photosynthesis, and in consequence that plant reactions to the infestation are more pronounced during the day. Consistent with this finding, lower model performance based on samples acquired during the dark period suggests that during the night, the plant's behavior when infested with spider mites is more similar to normal or uninfested behavior. Additionally, the improved model performance using daylight samples could also be related to increased activity of the insects during the day. Further investigation is needed to understand these differences better. To address the lack of a priori knowledge in the domain, a relatively big specer of different signal features extracted locally was initially considered at several window lengths. With the assessment of their mutual dependency as well as their impact in the classification process, the most relevant features for the stressed state discrimination were revealed. The overall results showed that the Hjorth complexity, characterizing the considered signal's curvature, contributes the most for the identification of the plant state within a frame of spider mites infestation. Aside from the Hjorth complexity, the interquartile range and the General Hurst Exponent also appear as valuable features for the plant state's discrimination. Each of these three features provides particular independent information at all the seven studied signal window lengths for the three sets of samples. Additionally, they are all portraying a temporal characterization of the signal. Hjorth complexity and IQR have previously been reported as a dominant feature for environmental stimuli discrimination when using the electrical plant response [11]. Frequency-related features, such as the color noises, also contribute to the classification of the plant state. Even though their impact is less dominant, the discriminative information they present could, however, be related to the expectations that an external stimulus is triggering a different color noise in the plant electrical response [10; 25; 26]. Different window lengths could also affect the discrimination power of a feature in a non-uniform manner. Additionally, the features found as the most important for the classification represent different window extents.