The feature extraction of plant electrical signal based on wavelet packet and neural network

Jing-xia, LU; Ding, Weimin

doi:10.1049/cp.2012.1417

Cited by 4 publications

(3 citation statements)

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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 spectral reflectance for each SMC was the average of the spectra data collected under the same sampling conditions. The spectrum was set as s(t), which represents the discrete signal with noise [23]. Seven layers of "db4" decompositions were applied to the original basic wavelet function through MATLAB and the wavelet coefficients of each layer were used to reconstruct the spectrum.…”

Section: Results Of Smc and Soil Spectral Datamentioning

confidence: 99%

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Jian

Jing-xia

et al. 2020

Agriculture

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Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields.

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“…Apart from having input layer and output layer, it also has one or more hidden layers. A 3-layer BP neural network can be done for any n-dimensional to m-dimensional mapping [2,[11][12][13][14][15][16] . The network training Complexity 9 method is called the error backpropagation method.…”

Section: Complexitymentioning

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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The feature extraction of plant electrical signal based on wavelet packet and neural network

Cited by 4 publications

References 0 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

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Feature Recognition of Crop Growth Information in Precision Farming

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