Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves

Abstract

• A CNN assisted CWT-based spectrogram strategy is proposed for chlorophyll content detection. • The first-order derivative was performed to capture detailed spectral information. • CWT was applied to obtain effective features from spectral data. • CNN model was applied to explore deep features hidden in spectral data. Visible and near-infrared spectroscopy is a nondestructive method for the chlorophyll content detection of potato crops, in which effective feature extraction is a crucial issue for detecting accuracy in the field. This study aimed to explore comprehensive features to improve the accuracy of chlorophyll content detection in potato leaves. A feature extraction method was proposed by assisting continuous wavelet transform (CWT)-based spectrogram and convolutional neural network (CNN) of the deep learning algorithm. In the experiments, the spectral data in the range of 325–1075 nm were measured in the field. A total of 314 potato leaf samples were collected, and the chlorophyll content was determined in four growth periods. The spectral features in the spatial domain and time–frequency domain were considered in data processing. First, we compared features of first-order derivative (FOD) and Savitzky–Golay smoothing (SG) in the spatial domain to select the preprocessing method. Second, in the time–frequency domain, CWT decomposes spectral data into a series of wavelet coefficient curves at various scales and wavelengths. We supposed that combining both scales and wavelengths of the wavelet coefficients could benefit the detection of the chlorophyll content in potato crops. Thus, the spectrograms were established by transforming 1D wavelet coefficient curves into 2D wavelet coefficient spectrograms. The CNN model was applied to explore potential and comprehensive features in the spectrograms. Finally, partial least squares models were established to compare the detection capability of three features including the spatial domain, wavelet coefficients by CWT, and spectrogram features by CNN. The modeling performances showed that the FOD features obtained the coefficient of determination of 0.7856 in the prediction set ( R P 2 ) and the root mean square error in the prediction set ( RMSEP ) with 3.9357; the FOD features performed better than the SG features ( R P 2 = 0.7734, RMSEP = 4.0460); The wavelet coefficients by CWT obtained R P 2 = 0.8293 and RMSEP = 3.5117. The PLS model with spectrogram features by the CNN model performed best and achieved R P 2 = 0.8749 and RMSEP = 3.0067. It preferably captured the deeper features of spectral data compared with features in the spatial domain. This research provides an effective method for leaf chlorophyll content detection in the precision management of potato crops.