## 4 Results and Discussion
### Web-Based Feature Extraction and Classification of Plant Electrical Signals  
Figure 12 shows the feature extraction web page. Firstly, user can select the start position (0 in the box in the figure) and the end position (1 in the box in the figure) of the plant electrical signals and then click the feature calculation button in the figure to calculate the 12 feature parameters.  
In order to provide automatic waveform recognition, the waveform extraction and template matching algorithm are implemented on the web platform for extracting the induced AP-like waveforms from the original signals with artifacts and background noise and determining whether the waveform is AP waveform [18]. The waveform extraction algorithm running on Spark can extract AP waveforms from 272 signals in 17 s, and a total of 357 possible AP waveforms are extracted.  
After extracting the AP-like waveforms of the plant electrical signals, it is still necessary to determine whether it is an AP waveform. Our system integrates the template-matching algorithm using the template library stored in HBase. In brief, we initially selected eight standard action potential signals as templates. Next, each AP-like waveform was compared with the templates in Figure 13 to calculate the similarity. Figure 14 displays typical examples of observed waveforms. The similarity-based algorithm allows us to determine the classification and update the template library. The details of algorithm were described in our previous works [18]. In the present work, we focused on spark-based parallel computing for the previous classification algorithm running on a desktop-based system.  
Figure 11: Add annotation for the plant electrical signals.  
Figure 12: Calculation features of plant electrical signals.  
Figure 13: Template Data.  
The dataset is the same as our previous work [18]. We tested 329 waveforms including 96 non-AP waveforms and 233 AP waveforms with template matching algorithm. In this work, we compared different thresholds and selected the threshold which showed the best performance. Here, Classification_Threshold is the value of similarity for identifying an AP-like waveform and deciding whether it is added as a new one to the template library. Moreover, Update_Threshold is the value of similarity for deciding whether an AP-like waveform merges with the old template to update the template. Using this proposed method, the 329 waveforms were identified and classified. However, the selection of Classification_Threshold is still prior knowledge-based. For example, if setting of Classification_Threshold is too high to few new templates added in the library, even leading to a low accuracy. When the Classification_Threshold = 0.91 and Update_threshold = 0.95, the classification accuracy is highest and up to 96%. Although the template library can be updated using template-matching algorithm, parallel template matching is not achieved completely through the Spark. When updating the template, the classification accuracy could up to 96%. Without updating of templates, the accuracy of the classification of plant electrical signals is only 89%. Each signal should be processed in serially because of the update of the template. In order to solve the parallelization of the template-matching algorithm, we can consider the multiple iterations, which updates part of the templates in each iteration and finally makes the classification accuracy and calculation time to achieve a better balance.