Improving Soil Quality Index Prediction by Fusion of Vis-NIR and pXRF spectral data

[1]  Nan Wang,et al.  Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network , 2023, Geoderma.

[2]  Yongsheng Hong,et al.  A comparison of multiple deep learning methods for predicting soil organic carbon in Southern Xinjiang, China , 2023, Comput. Electron. Agric..

[3]  Junjie Wang,et al.  Prediction of low Zn concentrations in soil from mountainous areas of central Yunnan Province using a combination of continuous wavelet transform and Boruta algorithm , 2023, International Journal of Remote Sensing.

[4]  M. Diago,et al.  Multi-sensor spectral fusion to model grape composition using deep learning , 2023, Inf. Fusion.

[5]  K. Garg,et al.  Diffuse reflectance spectroscopy (DRS) for rapid soil testing and soil quality assessment in smallholder farms , 2023, European Journal of Soil Science.

[6]  Tao Zeng,et al.  Source apportionment and source-specific risk evaluation of potential toxic elements in oasis agricultural soils of Tarim River Basin , 2023, Scientific Reports.

[7]  X. Lv,et al.  Improving soil organic matter estimation accuracy by combining optimal spectral preprocessing and feature selection methods based on pXRF and vis-NIR data fusion , 2023, Geoderma.

[8]  M. Vohland,et al.  Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale , 2023, Sensors.

[9]  Guofeng Wu,et al.  Spectral Features of Fe and Organic Carbon in Estimating Low and Moderate Concentration of Heavy Metals in Mangrove Sediments Across Different Regions and Habitat Types , 2022, SSRN Electronic Journal.

[10]  Nan Wang,et al.  Data mining of urban soil spectral library for estimating organic carbon , 2022, Geoderma.

[11]  Lantao Li,et al.  Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches , 2022, European Journal of Agronomy.

[12]  Qingyu Guan,et al.  A Monte Carlo simulation-based health risk assessment of heavy metals in soils of an oasis agricultural region in northwest China. , 2022, The Science of the total environment.

[13]  J. Amigo,et al.  Deep learning for near-infrared spectral data modelling: Hypes and benefits , 2022, TrAC Trends in Analytical Chemistry.

[14]  Xiaoran Fu,et al.  A TFA-CNN method for quantitative analysis in infrared spectroscopy , 2022, Infrared Physics & Technology.

[15]  Rujing Wang,et al.  Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil. , 2022, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[16]  C. Du,et al.  Applying convolutional neural networks (CNN) for end-to-end soil analysis based on laser-induced breakdown spectroscopy (LIBS) with less spectral preprocessing , 2022, Comput. Electron. Agric..

[17]  Xi Guo,et al.  Inversion of soil properties in rare earth mining areas (southern Jiangxi, China) based on visible–near-infrared spectroscopy , 2022, Journal of Soils and Sediments.

[18]  H. Emami,et al.  Assessing soil quality of pasture and agriculture land uses in Shandiz county, northwestern Iran , 2022, Ecological Indicators.

[19]  Minzan Li,et al.  Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves , 2022, Comput. Electron. Agric..

[20]  M. A. Munnaf,et al.  Fusion of visible-to-near-infrared and mid-infrared spectroscopy to estimate soil organic carbon , 2022, Soil and Tillage Research.

[21]  P. Mishra,et al.  A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks , 2022, Chemometrics and Intelligent Laboratory Systems.

[22]  Jian-min Zhou,et al.  A method combining FTIR-ATR and Raman spectroscopy to determine soil organic matter: Improvement of prediction accuracy using competitive adaptive reweighted sampling (CARS) , 2021, Comput. Electron. Agric..

[23]  V. Adamchuk,et al.  Evaluating the synergy of three soil spectrometers for improving the prediction and mapping of soil properties in a high anthropic management area: A case of study from Southeast Brazil , 2021 .

[24]  R. Vašát,et al.  Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil. , 2021, The Science of the total environment.

[25]  Anxiang Lu,et al.  Measurement of potentially toxic elements in the soil through NIR, MIR, and XRF spectral data fusion , 2021, Comput. Electron. Agric..

[26]  Héctor C. Goicoechea,et al.  Data Handling in Data Fusion: Methodologies and Applications , 2021 .

[27]  I. Yule,et al.  Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network , 2021 .

[28]  Dário Passos,et al.  Deep multiblock predictive modelling using parallel input convolutional neural networks. , 2021, Analytica chimica acta.

[29]  Puneet Mishra,et al.  A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit , 2021, Chemometrics and Intelligent Laboratory Systems.

[30]  Muhammad Abdul Munnaf,et al.  Fusion of Vis-NIR and XRF spectra for estimation of key soil attributes , 2021 .

[31]  R. Sakrabani,et al.  Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review , 2021, TrAC Trends in Analytical Chemistry.

[32]  Xianzhang Pan,et al.  Prediction of multiple soil fertility parameters using VisNIR spectroscopy and PXRF spectrometry , 2021 .

[33]  R. Naidu,et al.  Variability in plant trace element uptake across different crops, soil contamination levels and soil properties in the Xinjiang Uygur Autonomous Region of northwest China , 2021, Scientific Reports.

[34]  J. Roger,et al.  Recent trends in multi-block data analysis in chemometrics for multi-source data integration , 2021 .

[35]  S. Rezapour,et al.  Quantitative assessment of soil quality indices for urban croplands in a calcareous semi-arid ecosystem , 2021 .

[36]  R. Singh,et al.  Potential risk assessment of soil salinity to agroecosystem sustainability: Current status and management strategies. , 2020, The Science of the total environment.

[37]  K. Ghosh,et al.  Assessing the soil quality of Bansloi river basin, eastern India using soil-quality indices (SQIs) and Random Forest machine learning technique , 2020 .

[38]  J. Demattê,et al.  Soil texture prediction using portable X-ray fluorescence spectrometry and visible near-infrared diffuse reflectance spectroscopy , 2020 .

[39]  Douglas N. Rutledge,et al.  MBA-GUI: A chemometric graphical user interface for multi-block data visualisation, regression, classification, variable selection and automated pre-processing , 2020, Chemometrics and Intelligent Laboratory Systems.

[40]  Jean-Michel Roger,et al.  New data preprocessing trends based on ensemble of multiple preprocessing techniques , 2020 .

[41]  Minzan Li,et al.  Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization , 2020, Remote. Sens..

[42]  I. Kögel‐Knabner,et al.  The concept and future prospects of soil health , 2020, Nature Reviews Earth & Environment.

[43]  A. Mouazen,et al.  Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest , 2020 .

[44]  L. Ge,et al.  Recent developments on XRF spectra evaluation , 2020, Applied Spectroscopy Reviews.

[45]  Karina Maria Vieira Cavalieri-Polizeli,et al.  Soil quality: Evaluation of on-farm assessments in relation to analytical index , 2020, Soil and Tillage Research.

[46]  A. Karnieli,et al.  Using reflectance spectroscopy for detecting land-use effects on soil quality in drylands , 2020, Soil and Tillage Research.

[47]  A. Mouazen,et al.  Assessment of a soil fertility index using visible and near‐infrared spectroscopy in the rice paddy region of southern China , 2019, European Journal of Soil Science.

[48]  Zongming Wang,et al.  Soil quality assessment of croplands in the black soil zone of Jilin Province, China: Establishing a minimum data set model , 2019 .

[49]  Dora Neina,et al.  The Role of Soil pH in Plant Nutrition and Soil Remediation , 2019, Applied and Environmental Soil Science.

[50]  Congyan Lu,et al.  Rapid determination of moisture content in compound fertilizer using visible and near infrared spectroscopy combined with chemometrics , 2019, Infrared Physics & Technology.

[51]  Budiman Minasny,et al.  Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra , 2019, Geoderma.

[52]  A. Hartemink,et al.  Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties , 2019, European Journal of Soil Science.

[53]  Said Nawar,et al.  Can spectral analyses improve measurement of key soil fertility parameters with X-ray fluorescence spectrometry? , 2019, Geoderma.

[54]  Yibin Ying,et al.  DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis. , 2019, Analytica chimica acta.

[55]  H. Jia,et al.  Soil quality assessment under different land uses in an alpine grassland , 2018, CATENA.

[56]  Luuk Fleskens,et al.  Soil quality – A critical review , 2018 .

[57]  D. Zhou,et al.  Selecting the minimum data set and quantitative soil quality indexing of alkaline soils under different land uses in northeastern China. , 2018, The Science of the total environment.

[58]  R. Kerry,et al.  Assessment of soil quality indices for salt-affected agricultural land in Kurdistan Province, Iran , 2017 .

[59]  Kenneth A. Sudduth,et al.  Sensor data fusion for soil health assessment , 2017 .

[60]  Rasmus Bro,et al.  Extension of SO-PLS to multi-way arrays: SO-N-PLS , 2017 .

[61]  S. Singh,et al.  Soil quality index (SQI) as a tool to evaluate crop productivity in semi-arid Deccan plateau, India , 2016 .

[62]  Christoph Emmerling,et al.  Using Variable Selection and Wavelets to Exploit the Full Potential of Visible–Near Infrared Spectra for Predicting Soil Properties , 2016 .

[63]  T. Gomiero Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge , 2016 .

[64]  Rattan Lal,et al.  Towards a standard technique for soil quality assessment , 2016 .

[65]  A. Yadav,et al.  Soil quality index for evaluation of reclaimed coal mine spoil. , 2016, The Science of the total environment.

[66]  Rattan Lal,et al.  A standardized soil quality index for diverse field conditions. , 2016, The Science of the total environment.

[67]  O. Busto,et al.  Data fusion methodologies for food and beverage authentication and quality assessment - a review. , 2015, Analytica chimica acta.

[68]  Nicholas M. Holden,et al.  Evaluation of soil quality for agricultural production using visible–near-infrared spectroscopy , 2015 .

[69]  Kenneth A. Sudduth,et al.  Estimating a Soil Quality Index with VNIR Reflectance Spectroscopy , 2015 .

[70]  Tashpolat Tiyip,et al.  A soil quality assessment under different land use types in Keriya river basin, Southern Xinjiang, China , 2015 .

[71]  Vu Dang Hoang,et al.  Wavelet-based spectral analysis , 2014 .

[72]  A. Karnieli,et al.  A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use , 2014 .

[73]  N. Holden,et al.  Indices for quantitative evaluation of soil quality under grassland management , 2014 .

[74]  H. Bahrami,et al.  Assessment of soil quality indices in agricultural lands of Qazvin Province, Iran , 2014 .

[75]  Khan Towhid Towhid Osman,et al.  Soil Degradation, Conservation and Remediation , 2013 .

[76]  Jun Yang,et al.  Coupled expression of Cu/Zn-superoxide dismutase and catalase in cassava improves tolerance against cold and drought stresses , 2013, Plant signaling & behavior.

[77]  Thomas Scholten,et al.  The spectrum-based learner: A new local approach for modeling soil vis–NIR spectra of complex datasets , 2013 .

[78]  Weimin Ju,et al.  Using Wavelet Transform of Hyperspectral Reflectance Data for Extracting Spectral Features of Soil Organic Carbon and Nitrogen , 2012 .

[79]  Sergey Arzhantsev,et al.  Rapid limit tests for metal impurities in pharmaceutical materials by X-ray fluorescence spectroscopy using wavelet transform filtering. , 2011, Analytical chemistry.

[80]  A. McBratney,et al.  Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy , 2010 .

[81]  Rattan Lal,et al.  Challenges and opportunities in soil organic matter research , 2009 .

[82]  J. L. Darilek,et al.  Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. , 2009 .

[83]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

[84]  Budiman Minasny,et al.  A conditioned Latin hypercube method for sampling in the presence of ancillary information , 2006, Comput. Geosci..

[85]  Douglas L. Karlen,et al.  The Soil Management Assessment Framework , 2004 .

[86]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[87]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[88]  Pierre Goovaerts,et al.  Geostatistical modelling of uncertainty in soil science , 2001 .

[89]  John W. Doran,et al.  Simplified Method for Soil Particle-Size Determination to Accompany Soil-Quality Analyses , 2001 .

[90]  S. Wold,et al.  PLS regression on wavelet compressed NIR spectra , 1998 .

[91]  G. W. Thomas Soil pH and Soil Acidity , 1996, SSSA Book Series.

[92]  Jean. Steinier,et al.  Smoothing and differentiation of data by simplified least square procedure. , 1964, Analytical chemistry.

[93]  G. Bouyoucos A Recalibration of the Hydrometer Method for Making Mechanical Analysis of Soils1 , 1951 .

[94]  O. Dengiz,et al.  A soil quality index using Vis-NIR and pXRF spectra of a soil profile , 2022, CATENA.

[95]  Bo Zhang,et al.  Reducing the Moisture Effect and Improving the Prediction of Soil Organic Matter With VIS-NIR Spectroscopy in Black Soil Area , 2021, IEEE Access.

[96]  Abdul Mounem Mouazen,et al.  Development of a soil fertility index using on-line Vis-NIR spectroscopy , 2021, Comput. Electron. Agric..

[97]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[98]  Panos Panagos,et al.  Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach , 2014 .

[99]  R. V. Rossel,et al.  Visible and near infrared spectroscopy in soil science , 2010 .

[100]  A. The application of atomic absorption spectra to chemical analysis , 2022 .

[101]  R. Olea,et al.  Ordinary Kriging , 2022 .