Study on a fast EC measurement method of soda saline-alkali soil based on wavelet decomposition texture feature

Ren, Jianhong; Chen, Qiang; Ma, Dalong; Xie, Ruifeng; Zhu, Hui; Zang, Shuying

doi:10.1016/j.catena.2021.105272

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…In practice, discrete wavelet transform (DWT) is used to transform s (t) into the wavelet domain (Ren et al, 2021). DWT achieves multistage and multiscale transformation by transmitting the previous approximation subband C A to the quadrature mirror filters (Singh et al, 2021).…”

Section: Methodology Wavelet Decompositionmentioning

confidence: 99%

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021

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Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

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Section: Methodology Wavelet Decompositionmentioning

confidence: 99%

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021

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“…The energy feature was extracted from the LL sub-band obtained by wavelet decomposition ( Ren et al, 2021 ). In addition to the textural information, this feature was also able to amplify the dissimilarities that may be present between two regions and represent texture features in space from the point of energy distribution in the frequency domain with less computation and strong texture representation ability.…”

Section: Methodsmentioning

confidence: 99%

“…On this foundation, it is necessary to find a texture feature that can adapt to multi-temporal changes of rice to assist the VI to improve the sensitivity of LAI estimation, especially in the canopy closure stage and post-heading stage. Wavelet texture features can quantify the differences in image texture features ( Ren et al, 2021 ) and reveal the differences in the canopy structure in the process of rice growth.…”

Section: Introductionmentioning

confidence: 99%

“…The DWT, which is considered to be an efficient method for extracting hyperspectral features, is applied to quantify pigment concentration ( Blackburn, 2007 ), retrieve soil moisture ( Peng et al, 2013 ), and estimate LNC ( Li F. et al, 2019 ) and crop residue quality ( Sahadevan et al, 2014 ). In addition, energy is often used as a texture feature extracted by wavelet transform ( Salari and Ling, 1995 ; Banskota et al, 2013 ), which can represent texture features in space from the point of energy distribution in the frequency domain and has great texture representation capability ( Ren et al, 2021 ). The combination of texture features and spectral features has shown great potential in LAI estimation ( Zheng et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

et al. 2022

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Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.

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“…Because soil salinity and soil EC have a positive correlation and soil EC has a more prominent spectral response than soil salinity, the spectral information of soil EC has been widely used to estimate soil salinity [34]. In this study, the ASD Field Spec Pro FR spectrometer (Boulder, CO, USA) was used to collect the spectral data of soil samples in the lab.…”

Section: Spectral Acquisitionmentioning

confidence: 99%

Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)

et al. 2023

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Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas.

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Study on a fast EC measurement method of soda saline-alkali soil based on wavelet decomposition texture feature

Cited by 12 publications

References 40 publications

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)

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