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Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry.

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This study developed a polarization algorithm to remove sun glare from crop images, improving plant phenotyping accuracy. The method enhances seasonal yield monitoring and crop breeding by providing clearer data from imaging sensors.

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Optics

Background:

  • Plant phenotyping advances crop yield and breeding through automated monitoring.
  • Sun glare from leaves in field imaging creates noise, hindering accurate data collection.
  • Polarization data can differentiate surface reflection from internal leaf scattering.

Purpose of the Study:

  • To develop a facile algorithm using polarization data to decouple leaf surface glare from internal scattering.
  • To improve the accuracy of plant phenotyping data acquired by imaging sensors in field conditions.

Main Methods:

  • Combined data from a mast-mounted hyperspectral imaging polarimeter (HIP) and a fiber-based Mueller matrix bidirectional reflectance distribution function (mmBRDF) instrument.
  • Fitted mmBRDF data to a model to obtain parameters for simulation.
  • Trained a shallow neural network using simulated data to correct HIP sensor data based on vegetation indices and polarized light.

Main Results:

  • The developed algorithm significantly reduced errors and standard deviations in vegetation index calculations (GNDVI and red-edge reflection ratio).
  • An improvement of an order of magnitude or more in mean error (ϵ) was observed.
  • A reduction of 1.5 to 2.7 in standard deviation (ϵ) was achieved after applying the correction network.

Conclusions:

  • Polarization-based algorithms can effectively mitigate sun glare artifacts in hyperspectral imaging of crops.
  • This approach enhances the reliability of automated plant phenotyping for agricultural research.
  • Improved data quality supports more accurate crop health monitoring and accelerates breeding programs.