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. 2018 Sep 10;8(3):83.
doi: 10.3390/bios8030083.

Chemical Sensing Employing Plant Electrical Signal Response-Classification of Stimuli Using Curve Fitting Coefficients as Features

Shre Kumar Chatterjee  1 , Obaid Malik  2 , Siddharth Gupta  3
Affiliations

Chemical Sensing Employing Plant Electrical Signal Response-Classification of Stimuli Using Curve Fitting Coefficients as Features

Shre Kumar Chatterjee et al. Biosensors (Basel). .

Abstract

In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models-Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.

Keywords: classification; curve fitting; plant electrical signals.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Confusion matrix—showing different measures of classification.
Figure 2
Figure 2
Classification using Curve fit coefficients.
Figure 3
Figure 3
Four different curve fit types used to explore the coefficients as features for classification.
Figure 4
Figure 4
Raw plant electrical signal response after application of three types of stimuli.
Figure 5
Figure 5
R-squared values for Polynomial curve fitting.
Figure 6
Figure 6
R-squared values for Fourier curve fitting.
Figure 7
Figure 7
R-squared values for Gaussian curve fitting.
Figure 8
Figure 8
R-squared values for Exponential curve fitting.
Figure 9
Figure 9
Binary classification results using Polynomial Curve Fit Coefficients.
Figure 9
Figure 9
Binary classification results using Polynomial Curve Fit Coefficients.
Figure 10
Figure 10
Binary classification results using Fourier Curve Fit Coefficients.
Figure 11
Figure 11
Prospective test method using One Versus One classification decision tree.
See this image and copyright information in PMC

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