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

Lu et al., 2024 - Google Patents

A Vis/NIRS device for evaluating leaf nitrogen content using K-means algorithm and feature extraction methods

Lu et al., 2024

Document ID
12452491627993030975
Author
Lu M
Wang H
Xu J
Wei Z
Li Y
Hu J
Tian S
Publication year
Publication venue
Computers and Electronics in Agriculture

External Links

Snippet

Accurate assessing leaf nitrogen content (LNC) is crucial for actual production and fertilizer management. In this research, a portable device was designed to rapidly and non- destructively evaluate LNC with precision. Using hydroponically grown eggplants exposed …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light using near infra-red light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light for analysing liquids, e.g. polluted water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection circuits for computing concentration
    • G01N21/274—Calibration, base line adjustment, drift correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1293—Using chemometrical methods resolving multicomponent spectra
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/06—Illumination; Optics
    • G01N2201/062—LED's
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
    • G01J3/28—Investigating the spectrum
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
    • G01J3/02—Details
    • G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
    • G01J3/024—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows using means for illuminating a slit efficiently (e.g. entrance slit of a spectrometer or entrance face of fiber)

Similar Documents

Publication Publication Date Title
Zhang et al. Nondestructive measurement of soluble solids content in apple using near infrared hyperspectral imaging coupled with wavelength selection algorithm
Zhang et al. Fast prediction of sugar content in Dangshan pear (Pyrus spp.) using hyperspectral imagery data
Qi et al. Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data
Sun et al. Estimation of water content in corn leaves using hyperspectral data based on fractional order Savitzky-Golay derivation coupled with wavelength selection
Jia et al. A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat
Wang et al. Rapid detection of protein content in rice based on Raman and near-infrared spectroscopy fusion strategy combined with characteristic wavelength selection
Zhao et al. Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis
Zheng et al. Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics
Chen et al. Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data
Guo et al. Estimating leaf chlorophyll content in tobacco based on various canopy hyperspectral parameters
Guo et al. Detection model transfer of apple soluble solids content based on NIR spectroscopy and deep learning
Sun et al. Determination of moisture content in barley seeds based on hyperspectral imaging technology
Jin et al. Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory
Lu et al. A feasibility quantitative analysis of NIR spectroscopy coupled Si-PLS to predict coco-peat available nitrogen from rapid measurements
Ning et al. Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy
Wang et al. Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics
Meng et al. Fine hyperspectral classification of rice varieties based on self-attention mechanism
Song et al. Development of crop chlorophyll detector based on a type of interference filter optical sensor
Lu et al. A Vis/NIRS device for evaluating leaf nitrogen content using K-means algorithm and feature extraction methods
Qi et al. Rapid and non-destructive determination of soluble solid content of crown pear by visible/near-infrared spectroscopy with deep learning regression
Sonobe et al. Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves
Gao et al. Diagnosis of maize chlorophyll content based on hybrid preprocessing and wavelengths optimization
Wang et al. Origin identification of foxtail millet (Setaria italica) by using green spectral imaging coupled with chemometrics
Zhang et al. Advanced chemometrics toward robust spectral analysis for fruit quality evaluation
Yuan et al. Geographical origin identification of chinese tomatoes using long-wave Fourier-Transform Near-Infrared Spectroscopy combined with deep learning methods