Plant electrical signal classification based on waveform similarity
type: Item
instance of: scholarly article
title: Plant electrical signal classification based on waveform similarity
zbMATH Open document ID: 1461.92059
author: Xianqiang Yang
published in: Algorithms
publication date: 2019-03-26T00:00:00Z
review text: Summary: (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.
Mathematics Subject Classification ID: 92C80
zbMATH DE Number: 7042379
zbMATH Keywords: plant action potential (AP)
zbMATH Keywords: AP recognition
zbMATH Keywords: template matching
zbMATH Keywords: difference threshold
zbMATH Keywords: nonlinear features
zbMATH Keywords: AP classification
describes a project that uses: PETROPY
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a9040070
OpenAlex ID: W2537095933
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DOI: 10.3390/A9040070
description: scientific article; zbMATH DE number 7042379