The oscillatory bioelectrical signal from plants explained by a simulated electrical model and tested using Lempel–Ziv complexity

Cabral, Elaine C.; Pecora, Paula Cristina; Arce, Aldo Ivan Céspedes; Tech, Adriano Rogério Bruno; Costa, Ernane José Xavier

doi:10.1016/j.compag.2010.12.001

Cited by 23 publications

(20 citation statements)

References 9 publications

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“…In general, the average recognition rate of the method is 93.75%, and the recognition rate of it is not only higher than that of just using multiwavelet entropy structure feature; recognition rate is 83%. And the running efficiency of the program is fast, more than half [17,18].…”

Section: Analysis Of Resultmentioning

confidence: 99%

Feature Recognition of Crop Growth Information in Precision Farming

et al. 2018

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To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.

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Section: Analysis Of Resultmentioning

confidence: 99%

Feature Recognition of Crop Growth Information in Precision Farming

et al. 2018

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“…Amongst other signal processing methods the short time Fourier transform (STFT) for time frequency analysis and power spectrum estimation using parametric autoregressive models have also been explored in Tian et al [15]. Cabral et al [8] used complexity measures to characterize the nonlinearity and nonstationary behaviour of plant electrical signals inside and outside a Faraday cage. The independent component analysis (ICA) has been used in Huang et al [9] to separate the superimposed response generated from epidermal, mesophyll and guard cells.…”

Section: Previous Approaches Of Plant Electrical Signal Processingmentioning

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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The oscillatory bioelectrical signal from plants explained by a simulated electrical model and tested using Lempel–Ziv complexity

Cited by 23 publications

References 9 publications

Feature Recognition of Crop Growth Information in Precision Farming

Feature Recognition of Crop Growth Information in Precision Farming

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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