Electrical Signal Characterization of Aloe vera Var. Chinensis Using Non-Parametric and Parametric Signal Analysis

Recently, there is a renewed interest from the scientific community in the study of the electrical signal generated by plants due to its wide range of applications in agriculture, for example, environmental monitoring, detection of pests, diseases in crops, etc. Therefore, the aim of this work is to characterize the electrical signal of Aloe vera var. chinensis by using non-parametric and parametric signal analysis techniques, in order to extract some fundamental features which could be used in the design of a bio-dosimeter. Non-parametric analysis of the signal was carried out in the time, frequency, and time-frequency domains, using the short-time Fourier transform (STFT) and the wavelet transform in order to determine the different characteristics and frequency changes over time. Parametric analysis was then performed by using auto-regressive (AR) models for signal prediction and modeling, and in this case the coefficients of the model will be considered as fundamental features to be extracted. It has been identified that the majority of the signal energy is found in low frequencies, possibly associated with physiological processes or changes in the environment. Subsequently, some metrics like mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) were used in order to establish the capability of modeling the signal in its totality, considering that it is affected by the abrupt changes present in the signal. In this way, the relevance of combining both analyses is discussed in order to take their advantages for the benefit of the compression and feature extraction of the electrical signal of Aloe vera var. chinensis. This analysis allows the Aloe vera var. chinensis plant to be used for environmental monitoring, pest and disease detection in crops, or in a pattern recognition and signal classifier systems.