Analysis of Electrical Signals in Plant Physiological Responses: A Multi-Scale Adaptive Denoising Method Based on CEEMDAN-WST

Zihan Liu, Fangming Tian () and Feng Tan
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Zihan Liu: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Fangming Tian: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Feng Tan: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China

Agriculture, 2025, vol. 15, issue 21, 1-17

Abstract: Plant surface electrical signals are key representations for non-destructive monitoring of changes in cell membrane potential, enabling real-time reflection of physiological responses and regulatory processes under external stimuli. However, the low-frequency and weak-amplitude characteristics of these signals make them extremely susceptible to interference from multiple complex noise sources, such as environmental, power-line frequency, and inherent instrument noise. Existing denoising methods suffer from issues such as mode mixing and insufficient fidelity, hindering accurate extraction of genuine plant physiological information. This study proposes a novel denoising approach that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Soft Thresholding (WST). By decomposing and filtering noise components with adaptive thresholds based on the SURE criterion, the method achieves multi-scale decomposition and effective suppression of residual noise. Applied to surface electrical signals of maize leaves, the results demonstrated a 48% reduction in permutation entropy (PE) for the entire signal. In the resting potential segment, the root mean square (RMS) decreased by 28.91%, total energy dropped by 9.3%, and waveform stability improved. For the action potential segment, the full width at half maximum (FWHM) increased to 0.747, and although the peak amplitude slightly decreased, the waveform structure remained intact. Signal energy became more concentrated within the 0–2 Hz range, achieving efficient noise suppression and high signal fidelity. This method provides a reliable preprocessing technique for elucidating plant physiological mechanisms based on surface electrical signals and holds significant potential for real-time non-destructive monitoring and early warning systems in smart agriculture.

Keywords: CEEMDAN-WST; plant leaf surface electrical signals; signal denoising (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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