Nothing Special   »   [go: up one dir, main page]

Wei et al., 2022 - Google Patents

Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data

Wei et al., 2022

View HTML
Document ID
7892850840617827845
Author
Wei M
Canal Filho R
Tavares T
Molin J
Vieira A
Publication year
Publication venue
AI

External Links

Snippet

To obtain a better performance when modeling soil spectral data for attribute prediction, researchers frequently resort to data pretreatment, aiming to reduce noise and highlight the spectral features. Even with the awareness of the existence of dimensionality reduction …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01Social networking
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means

Similar Documents

Publication Publication Date Title
Ahmadi et al. Soil properties prediction for precision agriculture using visible and near-infrared spectroscopy: A systematic review and meta-analysis
Erler et al. Soil nutrient detection for precision agriculture using handheld laser-induced breakdown spectroscopy (LIBS) and multivariate regression methods (PLSR, Lasso and GPR)
Peng et al. Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy
Gholizadeh et al. Agricultural soil spectral response and properties assessment: effects of measurement protocol and data mining technique
Peng et al. Estimating soil organic carbon using VIS/NIR spectroscopy with SVMR and SPA methods
Jiang et al. Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy
Yao et al. Evaluation of six algorithms to monitor wheat leaf nitrogen concentration
Abdul Munnaf et al. Estimation of secondary soil properties by fusion of laboratory and on-line measured Vis–NIR spectra
Cozzolino The sample, the spectra and the maths—the critical pillars in the development of robust and sound applications of vibrational spectroscopy
Trontelj ml et al. Machine learning strategy for soil nutrients prediction using spectroscopic method
Jin et al. Prediction of soil-available potassium content with visible near-infrared ray spectroscopy of different pretreatment transformations by the boosting algorithms
Zhang et al. Leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors
S. Veum et al. Predicting profile soil properties with reflectance spectra via Bayesian covariate-assisted external parameter orthogonalization
Liu et al. Transferability of a visible and near-infrared model for soil organic matter estimation in riparian landscapes
Xu et al. Rapid determination of soil class based on visible-near infrared, mid-infrared spectroscopy and data fusion
Pei et al. Improving in-situ estimation of soil profile properties using a multi-sensor probe
Liu et al. Analysis of chlorophyll concentration in potato crop by coupling continuous wavelet transform and spectral variable optimization
Clingensmith et al. Predicting soil properties and interpreting vis-NIR models from across Continental United States
Li et al. Potential of VIS-NIR-SWIR spectroscopy from the Chinese Soil Spectral Library for assessment of nitrogen fertilization rates in the paddy-rice region, China
Sánchez-Esteva et al. Combining laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (Vis-NIRS) for soil phosphorus determination
Dhawale et al. Evaluating the precision and accuracy of proximal soil vis–NIR sensors for estimating soil organic matter and texture
Zhao et al. Calibration transfer based on affine invariance for NIR without transfer standards
Wang et al. The application of discrete wavelet transform with improved partial least-squares method for the estimation of soil properties with visible and near-infrared Spectral Data
Li et al. Combining variable selection and multiple linear regression for soil organic matter and Total nitrogen estimation by DRIFT-MIR spectroscopy
Wei et al. Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data