Scale Decomposition Research Articles

An adjustable active noise control algorithm based on Bark scale

An adjustable active noise control algorithm is proposed based on human auditory perception. Because of the FELMS algorithm, the Bark scale decomposition is used for simulating the decomposition of the human ears to the acoustic signal, and the real time response of the human ears to the noise is calculated. Residual filter is designed based on this real-time response so that the noise reduction effect is more in line with the human beings auditory perception. In the meantime, the adjustment parameter is set to adjust the amplitude-frequency response of the designed residual filter so that the noise reduction effect can be adjusted artificially under the condition of preserving the noise characteristics. Computer simulation results show that, match with the convention FXLMS algorithm, the algorithm put forward in this paper makes the residual noise, and the original noise has a similar energy change trend in Barks. This algorithm makes the noise reduction effect more aligned with the human auditory perception. It can retain the noise characteristics and artificially adjust the noise reduction effect.

Nonlinear Harmonic Electric Energy Metering System Based on the Wavelet Transform

Background: To solve the problem of accurate measurement of nonlinear harmonic electric energy, a method for improving harmonic electric energy measurement based on the discrete wavelet packet decomposition and reconstruction algorithm was proposed. Objectives: A reactive energy measurement method combined with Hilbert transformation was designed, and the frequency characteristics of wavelet function were discussed. An excessively high sampling rate could reduce the accuracy of harmonic power measurement. By setting the discrete wavelet packet transformation decomposition, the stepping of the wavelet form resulting from the excessively large wavelet packet decomposition scale was eliminated using the moncoband reconstruction algorithm. Methods: Experimental results showed that the detection error obtained by numerical simulation using the wavelet transform algorithm was close to the standard instrument, with the maximum reference error of 0.013%-0.030%. Results and Conclusion: The feasibility and measurement accuracy of the nonlinear harmonic electric energy metering system were verified, providing an effective method for accurately detecting the harmonic, interharmonic, and time-varying harmonic components of the electric power system.

Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary

With the industrial demand caused by multi-sensor image fusion, infrared and visible image fusion (IVIF) technology is flourishing. In recent years, scale decomposition methods have led the trend for feature extraction. Such methods, however, have low time efficiency. To address this issue, this paper proposes a simple yet effective IVIF approach via a feature-oriented dual-module complementary. Specifically, we analyze five classical operators comprehensively and construct the spatial gradient capture module (SGCM) and infrared brightness supplement module (IBSM). In the SGCM, three kinds of feature maps are obtained, respectively, by introducing principal component analysis, saliency, and proposing contrast estimation operators considered the relative differences of contrast information covered by the input images. These maps are later reconstructed through pyramidal transformation to obtain the predicted image. The IBSM is then proposed to refine the missing infrared thermal information in the predicted image. Among them, we improve the measurement operators applied to the exposure modalities, namely, the gradient of the grayscale images (2D gradient) and well-exposedness. The former is responsible for extracting fine details, and the latter is meant for locating brightness regions. Experiments performed on public datasets demonstrate that the proposed method outperforms nine state-of-the-art methods in terms of subjective visual and objective indicators.

Notes on molecular fragmentation and parameter settings for a dissipative particle dynamics study of a C10E4/water mixture with lamellar bilayer formation

The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A “bottom-up” decomposition of C10E4 into the smallest fragment molecules (particles) that satisfy chemical intuition leads to convincing simulation results which agree with experimental findings for bilayer formation and thickness. For integration of the equations of motion Shardlow’s S1 scheme proves to be a favorable choice with best overall performance. Increasing the integration time steps above the common setting of 0.04 DPD units leads to increasingly unphysical temperature drifts, but also to increasingly rapid formation of bilayer superstructures without significantly distorted particle distributions up to an integration time step of 0.12. A scaling of the mutual particle–particle repulsions that guide the dynamics has negligible influence within a considerable range of values but exhibits apparent lower thresholds beyond which a simulation fails. Repulsion parameter scaling and molecular particle decomposition show a mutual dependence. For mapping of concentrations to molecule numbers in the simulation box particle volume scaling should be taken into account. A repulsion parameter morphing investigation suggests to not overstretch repulsion parameter accuracy considerations.

Combination of Continuous Wavelet Transform and Successive Projection Algorithm for the Estimation of Winter Wheat Plant Nitrogen Concentration

Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth status of winter wheat and guiding field precision fertilization. In this study, the in situ hyperspectral reflectance data were measured by handheld SVC HR−1024I (SVC) passive field spectroradiometer and PNC were determined by the modified Kjeldahl digestion method. Continuous wavelet transform (CWT), successive projection algorithm (SPA) and partial least square (PLS) regression were combined to construct an efficient method for estimating winter wheat PNC. The main objectives of this study were to (1) use CWT to extract various wavelet coefficients under different decomposition scales, (2) use SPA to screen sensitive wavelet coefficients as independent variables and combine with PLS regression to establish winter wheat PNC estimation models, respectively, and (3) compare the precision of PLS regression models to find a reliable model for estimating winter wheat PNC during the growing season. The results of this paper showed that properly increasing the decomposition scale of CWT could weaken the impact of high-frequency noise on the prediction model. The number of wavelet coefficients has been significantly reduced after screened by SPA. The PNC estimation model (CWT–Scale6–SPA–PLS) based on the wavelet coefficients of the sixth decomposition scale most accurately predicted the PNC (the determination coefficient of the calibration set (Rc2) was 0.85. Root mean square error of the calibration set (RMSEc) was 0.27. The determination coefficient of the validation set (Rv2) was 0.84. Root mean square error of the validation set (RMSEv) was 0.28 and relative prediction deviation (RPD) was 2.47). CWT-Scale6-SPA-PLS can be used to predict PNC. The optimal winter wheat PNC prediction model based on CWT proposed in this study is a reliable method for rapid and nondestructive monitoring of PNC and provides a new technical method for precision nitrogen management.

Detecting the Third Heart Sound: Separation and Localization from Lung Sound using Intrinsic Time Scale Decomposition

Detecting the Third Heart Sound: Separation and Localization from Lung Sound using Intrinsic Time Scale Decomposition

Hyperspectral Estimation of Soil Organic Carbon Content Based on Continuous Wavelet Transform and Successive Projection Algorithm in Arid Area of Xinjiang, China

Soil organic carbon (SOC), an important indicator to evaluate soil fertility, is essential in agricultural production. The traditional methods of measuring SOC are time-consuming and expensive, and it is difficult for these methods to achieve large area measurements in a short time. Hyperspectral technology has obvious advantages in soil information analysis because of its high efficiency, convenience and non-polluting characteristics, which provides a new way to achieve large-scale and rapid SOC monitoring. The traditional mathematical transformation of spectral data in previous studies does not sufficiently reveal the correlation between the spectral data and SOC. To improve this issue, we combine the traditional method with the continuous wavelet transform (CWT) for spectral data processing. In addition, the feature bands are screened with the successive projection algorithm (SPA), and four machine learning algorithms are used to construct the SOC content estimation model. After the spectral data is processed by CWT, the sensitivity of the spectrum to the SOC content and the correlation between the spectrum and the SOC content can be significantly improved (p < 0.001). SPA was used to compress the spectral data at multiple decomposition scales, greatly reducing the number of bands containing covariance and enabling faster screening of the characteristic bands. The support vector machine regression (SVMR) model of CWT-R′ gave the best prediction, with the coefficients of determination (R2) and the root mean square error (RMSE) being 0.684 and 1.059 g∙kg−1, respectively, and relative analysis error (RPD) value of 1.797 for its validation set. The combination of CWT and SPA can uncover weak signals in the spectral data and remove redundant bands with covariance in the spectral data, thus realizing the screening of characteristic bands and the fast and stable estimation of the SOC content.

Composite Fault Feature Enhancement Approach for Rolling Bearings Grounded on ITD and Entropy-based Weight Method

Aiming to precisely identify a compound fault of rolling bearing, the paper has contributed a fault characteristic enhancement method by combing entropy weight method (EWM) and intrinsic time scale decomposition (ITD). Firstly, to effectively segregate frequency components in vibration signals, proper rotation components (PRCs) were obtained by decomposing vibration signals based on ITD. Secondly, in view of the fact that amplitude, variance and correlation coefficient vary greatly in a bearing fault accompanied by impact components, parameter evaluation indexes were brought in to depict the fault characteristics of PRCs, including average, variance, correlation coefficient, margin factor, kurtosis, impulse factor, peak factor and so on. Thirdly, weight coefficient of each parameter index was calculated by entropy weight method and the characteristics of each PRC highlighted based on that. Finally, the signals were reconstructed according to the PRCs whose characteristics had been enhanced. Meanwhile reconstructed signals were denoised with singular differential spectrum (SDS) to reduce the influence of noise components, and then the type of compound fault was distinguished grounded on the frequency spectrum. To further prove the efficiency of proposed method, it is compared with other methods (SDS, ITD + entropy method). The result indicates that the proposed method can further highlight the characteristic information of compound faults of bearing and embody more exact identification and judgment on the type of faults.

Fault Identification Technology of 66 kV Transmission Lines Based on Fault Feature Matrix and IPSO-WNN

Due to the barely resonant earthed system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) is proposed to address the difficulties in extracting and detecting characteristic parameters. The maximum matrix of the dbN wavelet was used to determine its decomposition scale and construct the fault characteristic matrix based on the energy values of frequency bands. The decomposition scale of the dbN wavelet was determined by the modulus maximum matrix to ensure the integrity of fault information. The fault feature matrix was then constructed based on the energy values of frequency bands and the fault feature was accurately extracted. In this research, aiming at the problems such as slow convergence speed and a tendency to fall into local minima, the WNN algorithm is enhanced with the IPSO algorithm. This significantly increased the convergence speed of the identification model and its ability to discover the global optimal solution. The simulation results demonstrate that this method can effectively and accurately identify the fault type with high identification accuracy, quick identification, and robust adaptability. Under challenging working conditions, it is capable of accurately identifying the fault type of 66 kV transmission lines.

Multi-scale flow structure and its effect on momentum flux in the coastal marine atmospheric boundary layer

To accurately calculate the turbulent exchange coefficient, the contribution of multi-scale turbulent transportation needs to be considered, especially in the complex terrain of the coastal area. In September 2019, a comprehensive observation experiment on the offshore atmospheric boundary layer was carried out at the Yangmeikeng Ecological Monitoring Station and Sai Chung Gulf. Through scale decomposition, it is shown that the turbulent motion in the atmospheric boundary layer in the coastal area is affected by the underlying surface, such as that of the coastal land or the sea–land boundary. This is the main reason behind the phenomenon whereby different scales make different contributions to momentum flux. Different multi-scale characteristics of turbulent structures on the underlying surface affect the drag coefficient. Through wavelet transform and finite element method, the characteristics of the multi-scale flow structures produced by the complicated offshore terrain are analysed. It is found that large-scale flow structures enhance the pulsating intensity at the small scale, but the large-scale coherence characteristics are different from those at the small scale. In summary, in comparing these three sites, the flux exchange on the roof is greatest, followed by that on the tower. In the Gulf, the flux exchange is mainly dependent on small-scale structures, which are linked with the smallest values.

Использование морфометрических величин при изучении рельефа поверхности рентгенооптических элементов

We studied the use of morphometric variables (maximal curvature, minimal curvature, mean curvature, topographic index, etc.) for study of the surface of X-ray optical elements. We performed calculations on digital elevation models of a spherical concave substrates: primordial and smoothed digital elevation models, before and after technological operations (mechanical lapping, axisymmetric surface shape correction, ion beam figuring). We have demonstrated a visual display of weakly expressed topography inhomogeneities, incl. which are not displayed on the maps of the original digital elevation models. The consideration included the study of both the measured samples and the errors in the formation of the digital elevation model (artifacts from the recording system and from the inhomogeneity of the medium), as well as the features of scale decomposition when using the universal spectral-analytical method.

Adaptive scale decomposition and weighted multikernel correntropy for wheelset axle box bearing diagnosis under impact interference

In this paper, a novel diagnosis method based on adaptive scale decomposition and weighted multikernel correntropy (ASD-WMKC) is developed to diagnose the faults of wheelset axle box bearing under strong wheel-rail impact interference and background noise. The ASD method is utilized to extract the complete fault characteristic signal of axle box bearing, in which the CHIMDO method is applied to restrict the overshoot and undershoot problems, an optimal shape approximation extension method is developed to suppress the end effects, and an adaptive decomposition scale construction method is proposed to overcome the mode mixing and splitting problems of decomposition. The WMKC is applied to enhance the weak fault characteristic signal and suppress the impact interferences, in which the multikernel correntropy combined with a 2D Gaussian kernel is utilized to overcome the defects of weak adaptability and poor anti-interference ability of correntropy, respectively. Simulation and application studies show that the proposed ASD-WMKC method outperforms the comparative methods and can extract the fault characteristic signal of axle box bearing with the highest completeness and SNR under strong wheel-rail impact interference and background noise.

Network traffic anomaly detection method based on multi-scale residual classifier

In view of the current research seldom consider the multi-scale characteristics of network traffic, which may lead to an inaccurate classification of anomalies and a high false alarm rate. In this paper, a network traffic anomaly detection method based on the multi-scale residual classifier (MSRC) is proposed. We use sliding windows to divide the network traffic into subsequences with different observation scales, use the wavelet transform technology to obtain the time–frequency information of each subsequence on multiple decomposition scales, design a stacked automatic encoder (SAE) to learn the distribution of input data, calculate the reconstruction error vector by using the constructed feature space, and learn the feature information of different scales in the reconstruction error vector by using the multipath residual group, and complete traffic anomaly detection through the lightweight classifier. Experimental results show that the detection performance of the proposed method for abnormal network traffic is improved compared with the traditional method. It is proved that large observation scales and more transformation scales have positive effects on discovering the potential diversity information in the original network traffic.

An Indirect Inversion Scheme for Retrieving Toxic Metal Concentrations Using Ground-Based Spectral Data in a Reclamation Coal Mine, China

A reclamation coal mine in Baishui County of Shaanxi Province, China, was selected as the study area to develop a fast survey method for estimating soil heavy metal concentrations using spectral data. A portable object spectrometer manufactured by Analytical Spectral Devices (ASD) was used to measure soil spectral reflectance, and an X-ray fluorescence device was utilized to obtain the content of heavy metals. The Savitzky-Golay filter, first derivative reflectance (FDR), second derivative reflectance (SDR), continuum removal (CR), and continuous wavelet transform (CWT) were used to transform the original reflectance (OR) spectra for enhancing the spectral characteristics, respectively. Furthermore, correlation analysis was introduced to determine the characteristic bands and the correlations of heavy metals. Partial least squares regression (PLSR), extremely learning machine (ELM), random forest (RF), and support vector machine (SVM) were implemented for quantitatively determining relations between heavy metal contents and spectral reflectance. The outcomes demonstrated that the spectral transformation methods could effectively capture the characteristic bands and increase the relations between heavy metal contents and spectral reflectance. The relation between Fe and Ni was close with a relatively high correlation coefficient (r = 0.741). RF combined with CWT at the decomposition scales of 9 demonstrated the best performance with the highest Rv2 (0.71) and the lowest RMSEv (1019.1 mg/kg) for inferring Fe content. Ni content was inferred based on the close relationship between Fe and Ni. The result of RF was better than other methods with the highest Rv2 (0.69) and the lowest RMSEv (1.94 mg/kg) for estimating Ni concentration. Therefore, the RF model was chosen for mapping Fe and Ni contents in the study area. The present study revealed that the indirect inversion methods using spectral data can be effectively used to predict heavy metal concentrations. The outcomes supply a new perspective for retrieving heavy metal content based on hyperspectral remotely sensed technology.

Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches

The vertical leaf nitrogen (N) distribution in summer maize is believed to be an important adaptive reaction to crop physiology and ecosystem functions. Therefore, it is of great significance to understand the vertical characteristics of leaf N concentration (LNC) across summer maize canopies for crop growth and production. However, the effect of vertical canopy position on LNC and subsequently leaf hyperspectral characteristics of the entire spectrum (350–2500 nm) for summer maize is poorly understood. The purpose of this work was to quantitatively study the effects of the N nutrition on vertical distribution of LNC, identify the sensitive layer and effective wavelength of leaves, and establish a in situ leaf spectrum monitoring model considering vertical distribution of LNC. Six N field trials were conducted for four consecutive years (2017–2020). In addition, the data of 30 farmers' conventional farmland management in 2020 were collected to check the robustness of the constructed optimal estimation model for LNC prediction. Leaf spectral measurements together with LNC were studied at three vertical leaf positions on the crop stem: upper, middle and lower. Spectral reflectance was processed by continuous wavelet transform (CWT); partial least square (PLS) was used to analyze the relationships between LNC of different layers and spectral reflectance. Field sampling indicated that LNC had a vertical distribution pattern and an evident decline from the upper to lower layer. CWT technique can significantly increase the prediction accuracy of LNC at different layers, and the best decomposition scale is CWT-1. The CWT-1-PLS model for predicting vertical LNC distribution had achieved relatively higher accuracy than that based on the full range of the raw hyperspectral reflectance (R), the LNC determination coefficient (R2val) of the validation dataset was 0.832, 0.857 and 0.811 for the upper, middle and lower layer, and the relative percentage deviations (RPDval) were 2.444, 2.432, and 2.181, respectively. Eventually, ten bands were selected as the effective wavelengths for predicting the vertical LNC distribution in the upper, middle and lower layer, respectively. Newly developed CWT-1-PLS model using the effective wavelengths for LNC prediction in different layers also performed well (RPDval>2.0) based on the field experiments validation. Moreover, the validation at the farmers’ fields also showed fine precision for upper (RPDval=2.012), middle (RPDval=2.137) and lower (RPDval=1.881) layer LNC prediction. These results are of great significance for the study of summer maize leaf reflectance modelling, especially for the studies of integrating hyperspectral measurements and leaf traits data.

Seismic Signal Denoising Based on Adaptive Wavelet Modulus Maximum Method

Seismic noise suppression plays an important role in seismic data processing and interpretation. Aiming at remedying the problem of low quality of seismic data acquired by a seismometer, a novel denoising method based on wavelet maximum modulus and an adaptive threshold is designed. This adaptive wavelet maximum modulus (ATWMM) seismic signal noise reduction method is using the opposite polarity of the Lipschitz index with seismic signal and noise to extract the seismic signal of original data. Setting adaptive thresholding function related to wavelet decomposition scale to solve the problem of effective signal losing on a small decomposition scale. The experimental results show that the ATWMM method can extract more seismic signal from the noisy seismic data. Using RMSE and SNR evaluation, the synthetic Ricker seismic dataset experimental results show that the indexes are 0.0502 and 20.1617 dB. Compared with the wavelet modulus maximum (WMM) method, it has a 25.7% reduction and 14.6% increase. The real-field seismic data came from the JZW51 seismic monitoring station in China experimental results indicate that the proposed ATWMM method is effective in seismic signal denoising with SNR above 30.1855 dB of approximately 15.8% enhancement compared with the WMM method, that has improvement for quality of seismic data.

Hypersonic shock wave and turbulent boundary layer interaction in a sharp cone/flare model

A direct numerical simulation of hypersonic Shock wave and Turbulent Boundary Layer Interaction(STBLI) at Mach 6.0 on a sharp 7° half-angle circular cone/flare configuration at zero angle of attack is performed. The flare angle is 34° and the momentum thickness Reynolds number based on the incoming turbulent boundary layer on the sharp circular cone is Reθ = 2506. It is found that the mean flow is separated and the separation bubble occurring near the corner exhibits unsteadiness. The Reynolds analogy factor changes dramatically across the interaction, and varies between 1.06 and 1.27 in the downstream region, while the QP85 scaling factor has a nearly constant value of 0.5 across the interaction. The evolution of the reattached boundary layer is characterized in terms of the mean profiles, the Reynolds stress components, the anisotropy tensor and the turbulence kinetic energy. It is argued that the recovery is incomplete and the near-wall asymptotic behavior does not occur for the hypersonic interaction. In addition, mean skin friction decomposition in an axisymmetric turbulent boundary layer is carried out for the first time. Downstream of the interaction, the contributions of transverse curvature and body divergence are negligible, whereas the positive contribution associated with the turbulence kinetic energy production and the negative spatial-growth contribution are dominant. Based on scale decomposition, the positive contribution is further divided into terms with different spanwise length scales. The negative contribution is analyzed by comparing the convective term, the streamwise-heterogeneity term and the pressure gradient term.

Classifying decomposition and wavelet coorbit spaces using coarse geometry

This paper is concerned with the study of Besov-type decomposition spaces, which are scales of spaces associated to suitably defined coverings of the euclidean space Rd, or suitable open subsets thereof. A fundamental problem in this domain, that is currently not fully understood, is deciding when two different coverings give rise to the same scale of decomposition spaces.In this paper, we establish a coarse geometric approach to this problem, and show how it specializes for the case of wavelet coorbit spaces associated to a particular class of matrix groups H<GL(Rd) acting via dilations. These spaces can be understood as special instances of decomposition spaces, and it turns out that the question whether two different dilation groups H1,H2 have the same coorbit spaces can be decided by investigating whether a suitably defined map ϕ:H1→H2 is a quasi-isometry with respect to suitably defined word metrics. We then proceed to apply this criterion to a large class of dilation groups called shearlet dilation groups, where this quasi-isometry condition can be characterized algebraically. We close with the discussion of selected examples.

Influence of Synthetic Jets on Multiscale Features in Wall-Bounded Turbulence

This experimental research focuses on the impacts of submerged synthetic jets on a fully-developed turbulent boundary layer (TBL) under a drag reduction working case. Two-dimensional velocity vectors in the flow field are captured with the aid of a particle image velocimetry (PIV) system. Proper orthogonal decomposition (POD) analyses provide evidence that synthetic jets notably attenuate the induction effect of prograde vortex on the low-speed fluid in large-scale fluctuation velocity field, thereby weakening the bursting process of near-wall turbulent events. Furthermore, the introduced perturbance redistributes the turbulent kinetic energy (TKE) and concentrates the TKE onto small-scale coherent structures. Modal time coefficients in various orders of POD are divided into components of multiple frequency bands by virtue of complementary ensemble empirical mode decomposition (CEEMD). It is found that the turbulence signals are shifted from low-frequency to high-frequency bands thanks to synthetic jets, thus revealing the relationship between scales and frequency bands. One further method of scale decomposition is proposed, that is, the large-scale fluctuating flow field will be obtained after removing the high-frequency noise data with the help of continuous mean square error (CMSE) criterion.

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features.

Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best “CWT spectra” model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of “CWT-9 spectra + texture,” and its determination coefficients (R2val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R2val = 0.66, RMSEval = 0.34), the R2val increased by 0.24. Different from our hypothesis, the combined feature based on “CWT spectra + color + texture” cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.