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

Abstract Wavelet texture features can well describe the cracking of salt-affected clayey soils on different decomposition scales since the desiccation cracking is considered as a common phenomenon and mainly determined by the salt content. To establish the relationship between wavelet texture features and the salinity of soda saline-alkali soils, 200 soil samples were selected in Songnen Plain of China and the crack images of the samples were processed uniformly. The 4-levels orthogonal wavelet decompositions were performed based on coiflet-1 wavelet basis function using the grayscale images. After that, correlation analysis was carried out between electrical conductivity (EC) and wavelet texture features (energy and L1 norm) under different decomposition levels. The results indicate that the poor relationship between low-frequency texture features and EC is hardly affected by the decomposition levels, but the correlation coefficients between high-frequency wavelet texture features and EC of soil samples increases significantly with decomposition levels. Besides, 100 calibration samples were used to establish the regression models and the results show that both energy and L1 norm are exponentially correlated with EC of soil samples especially for those from 90° high-frequency decomposition results with R2 of the exponential models of 0.90 and 0.84. A fast EC measurement method for predicting soil EC was then proposed and verified by the other 100 soil samples. The fitting results show very high prediction accuracy when EC was calculated by the energy from 90° with R2 of 0.91 and ratio of performance to deviation (RPD) of 4.73 and L1 norm from 90° with R2 of 0.85 and RPD of 3.53. Moreover, the fitting results based on the mean texture features calculated from 0°, 90° and 135° high-frequency decomposition results are also relatively good (R2 of 0.88 and RPD of 4.13 for energy, R2 of 0.81 and RPD of 3.36 for L1 norm).

[1]  Sarita P. Ambadekar,et al.  Adaptive Digital Image Watermarking Technique Through Wavelet Texture Features , 2020 .

[2]  N. Shokri,et al.  Salinity effects on cracking morphology and dynamics in 3‐D desiccating clays , 2014 .

[3]  J. Macgregor,et al.  Image texture analysis: methods and comparisons , 2004 .

[4]  Yu Qing-chun Research y on strength of Song-Nen Plain soda-saline soil , 2008 .

[5]  Yi Ma,et al.  Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe (Yellow) River Estuary wetland , 2019, Acta Oceanologica Sinica.

[6]  G. S. Dasog,et al.  SHRINK-SWELL POTENTIAL AND CRACKING IN CLAY SOILS OF SASKATCHEWAN , 1988 .

[7]  A. Padma,et al.  Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images , 2016, Int. J. Imaging Syst. Technol..

[8]  V. Aksenov,et al.  Strength Characteristics of Frozen Saline Soils , 2003 .

[9]  P. Bullock,et al.  THE EFFECT OF SOIL COMPOSITION AND ENVIRONMENTAL FACTORS ON THE SHRINKAGE OF SOME CLAYEY BRITISH SOILS , 1980 .

[10]  R. K. Misra,et al.  Sensitivity of EM38 in determining soil water distribution in an irrigated wheat field , 2011 .

[11]  Aoife A. Gowen,et al.  Spatial‐spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images , 2020, Journal of Chemometrics.

[12]  Suchitra Khoje,et al.  Comparative performance evaluation of fast discrete curvelet transform and colour texture moments as texture features for fruit skin damage detection , 2015, Journal of Food Science and Technology.

[13]  Meng-Jia Lian,et al.  Texture feature extraction of gray-level co-occurrence matrix for metastatic cancer cells using scanned laser pico-projection images , 2018, Lasers in Medical Science.

[14]  Junfeng Jing,et al.  Automatic classification of woven fabric structure based on texture feature and PNN , 2014, Fibers and Polymers.

[15]  James D. Oster,et al.  Salt Transport in Cracking Soils: Bromide Tracer Study , 1997 .

[16]  Jing Li,et al.  Improving Deep Learning Feature with Facial Texture Feature for Face Recognition , 2018, Wireless Personal Communications.

[17]  Jianhua Ren,et al.  Study of an on-line measurement method for the salt parameters of soda-saline soils based on the texture features of cracks , 2016 .

[18]  E. C. Stegman,et al.  Water flow and salt transport in cracking clay soils of the Imperial Valley, California , 2007 .

[19]  Arvind R. Yadav,et al.  Hardwood species classification with DWT based hybrid texture feature extraction techniques , 2015 .

[20]  Sheerman-ChaseA.,et al.  Seasonal slope movements in an old clay fill embankment dam , 2013 .

[21]  Abdelbasset Brahim,et al.  Texture analysis using complex wavelet decomposition for knee osteoarthritis detection: Data from the osteoarthritis initiative , 2018, Comput. Electr. Eng..

[22]  Thomas E. Fenton,et al.  Effect of Daily Soil Temperature Fluctuations on Soil Electrical Conductivity as Measured with the Geonics® EM-38 , 2004, Precision Agriculture.

[23]  Adnan Khashman,et al.  Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations , 2017 .

[24]  Adel Bakhshipour,et al.  Weed segmentation using texture features extracted from wavelet sub-images , 2017 .

[25]  Suchitra Khoje,et al.  Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms. , 2018, Journal of texture studies.

[26]  S. Leroueil,et al.  The Effects of Salinity and Shear History on The Rheological Characteristics of Illite-Rich and Na-Montmorillonite-Rich Clays , 2012, Clays and Clay Minerals.

[27]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[28]  R. F. Allbrook,et al.  Relationships between shrinkage indices and soil properties in some New Zealand soils , 2002 .

[29]  Xiaojie Li,et al.  Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation , 2019, Remote. Sens..

[30]  Stefano Utili,et al.  Investigation by limit analysis on the stability of slopes with cracks , 2013 .

[31]  A. Santis,et al.  Bypass flow, salinization and sodication in a cracking clay soil , 2004 .

[32]  Ewa Ropelewska,et al.  Classification of the seeds of traditional and double‐low cultivars of white mustard based on texture features , 2019, Journal of Food Process Engineering.

[33]  C. Smith,et al.  Shrinkage and Atterberg limits in relation to other properties of principal soil types in Israel , 1985 .

[34]  X Ou,et al.  Skin image retrieval using Gabor wavelet texture feature , 2016, International journal of cosmetic science.

[35]  H. Lan,et al.  Porewater salinity effect on flocculation and desiccation cracking behaviour of kaolin and bentonite considering working condition , 2019, Engineering Geology.

[36]  Chen Hui-e An Experiment Study of the Fundamental Property of the Carbonate-saline Soil in West of Jilin Province , 2011 .

[37]  G. Ross,et al.  RELATIONSHIPS OF SPECIFIC SURFACE AREA AND CLAY CONTENT TO SHRINK-SWELL POTENTIAL OF SOILS HAVING DIFFERENT CLAY MINERALOGICAL COMPOSITIONS , 1978 .

[38]  Y. Cui,et al.  Effect of salt concentration on desiccation cracking behavior of GMZ bentonite , 2017, Environmental Earth Sciences.

[39]  A. Keshmiri,et al.  Patterns of Desiccation Cracks in Saline Bentonite Layers , 2015, Transport in Porous Media.

[40]  Rongbiao Zhang,et al.  Rapid Detection of Rice disease Using Microscopy Image identification based on the synergistic judgment of TS features and DT-CM method. , 2019, Journal of the science of food and agriculture.

[41]  Xiaonan Luo,et al.  A simple texture feature for retrieval of medical images , 2018, Multimedia Tools and Applications.

[42]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.