Study and evaluation of plant electrical signal processing method

Jing-xia, LU; Ding, Weimin

doi:10.1109/cisp.2011.6100693

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Although there have been few recent attempts on signal processing, feature extraction and statistical analysis using plant electrical responses [12][13][14][15][16][17][18], there has been no attempt to associate features extracted from plant electrical signals to different external stimuli. The focus of our work is to address this gap.…”

Section: Introductionmentioning

confidence: 99%

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Chatterjee

Das

Maharatna

et al. 2015

J. R. Soc. Interface.

104

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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli-sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

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Section: Introductionmentioning

confidence: 99%

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Chatterjee

Das

Maharatna

et al. 2015

J. R. Soc. Interface.

104

View full text Add to dashboard Cite

show abstract

“…The IIR filtered plant signals are decomposed via WPT as shown in Figure 4 where the spectrum of the signal is halved at each level using low-pass and high-pass filter banks. Now, each of the wavelet packet nodes will consist equal but different parts of the spectrum and the energy content at each node can be estimated using (5), where P is the number of samples at each leaf node (i).…”

Section: Wavelet Energy Criterion For Optimizing the Iir Filtersmentioning

confidence: 99%

“…Also, in order to avoid removal of some significant frequency components containing vital information of the actual biological response in the recorded raw plant signal (as a side effect of filtering), there is a need to investigate an optimal bandwidth of its frequency spectrum. The plant signals are generally weak in nature [5], and are therefore prone to get overwhelmed by noise, artifacts and drifts, which motivates the design of a robust filtering technique, to get rid of these external disturbances. This will ensure that the pre-stimulated signals, recorded under different experimental conditions lie in a common platform, for the study of the stimulated response in plants.…”

Section: Introductionmentioning

confidence: 99%

Drift removal in plant electrical signals via IIR filtering using wavelet energy

Das

Ajiwibawa

Chatterjee

et al. 2015

Computers and Electronics in Agriculture

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Plant electrical signals often contains low frequency drifts with or without the application of external stimuli. Quantification of the randomness in plant signals in a stimulus-specific way is hindered because the knowledge of vital frequency information in the actual biological response is not known yet. Here we design an optimum Infinite Impulse Response (IIR) filter which removes the low frequency drifts and preserves the frequency spectrum corresponding to the random component of the unstimulated plant signals by bringing the bias due to unknown artifacts and drifts to a minimum. We use energy criteria of wavelet packet transform (WPT) for optimization based tuning of the IIR filter parameters. Such an optimum filter enforces that the energy distribution of the pre-stimulus parts in different experiments are almost overlapped but under different stimuli the distributions of the energy get changed. The reported research may popularize plant signal processing, as a separate field, besides other conventional bioelectrical signal processing paradigms.

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“…5 minutes of light, 10 minutes of dark). The following schematic diagram in Figure 2 A digital low-pass filter with a cut-off frequency 1 Hz was provided to eliminate any noise associated with the measurement, as in [13][14][15][16][17][18], [39] it is reported that plant signals are slow oscillatory signals at a very low frequency. Light Emitting Diode (LED) light source were used for providing white-light at maximum brightness.…”

Section: Experimental Data and Model Estimationmentioning

confidence: 99%

“…A detailed analysis of plant electrophysiological systems is documented in [12]. Although there were a few attempts to understand the nature of such electrical signals using signal processing techniques [13][14][15][16][17][18][19][20], rigorous models correlating the characteristics of such signals with the externally applied stimuli are yet to be developed.…”

Section: Introductionmentioning

confidence: 99%

Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants

et al. 2014

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In this paper, system identification approach has been adopted to develop a novel dynamical model for describing the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf (Laurus nobilis) plant. More specifically, the target is to predict the characteristics of the input light stimulus (in terms of on-off timing, duration and intensity) from the measured electrical response-leading to an inverse problem. We explored two major classes of system estimators to develop dynamical models-linear and nonlinear-and their several variants for establishing a forward and also an inverse relationship between the light stimulus and plant electrical response. The best class of models are given by the Nonlinear Hammerstein-Wiener (NLHW) estimator showing good data fitting results over other linear and nonlinear estimators in a statistical sense. Consequently, a few set of models using different functional variants of NLHW has been developed and their accuracy in detecting the on-off timing and intensity of the input light stimulus are compared for 19 independent plant datasets (including 2 additional species viz. Zamioculcas zamiifolia and Cucumis sativus) under similar experimental scenario. © 2014 Elsevier B.V. All rights reserved

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Study and evaluation of plant electrical signal processing method

Cited by 11 publications

References 3 publications

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Drift removal in plant electrical signals via IIR filtering using wavelet energy

Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants

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