Document Type
Article
Publication Date
10-15-2016
Department
Department of Biological Sciences
Abstract
(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.
Publication Title
Algorithms
Recommended Citation
Chen, Y.,
Zhao, D.,
Wang, Z.,
Wang, Z.,
Tang, G.,
&
Huang, L.
(2016).
Plant electrical signal classification based on waveform similarity.
Algorithms,
9(4), 70.
http://doi.org/10.3390/a9040070
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1888
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). Publisher’s version of record: https://doi.org/10.3390/a9040070