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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
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Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and

Mohsen Mirzaei1, Jochem Verrelst2, Safar Marofi3

  • 1Environmental Pollutions, Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Malayer 65719-95863 Iran.

Remote Sensing
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

In-field spectroscopy effectively monitors heavy metals (Cu, Zn, Pb, Cr, Cd) in grapevine leaves by analyzing spectral patterns. This method offers a reliable approach for assessing plant health and ensuring food safety in agricultural ecosystems.

Keywords:
MLRPLSSVMfield spectroscopygrapevineheavy metalshyperspectral

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Plant Physiology

Background:

  • Heavy metal contamination in food-producing ecosystems poses risks to human health.
  • Plant physiochemical characteristics, influenced by heavy metals, alter leaf optical properties.
  • In-field spectroscopy offers a non-destructive method for monitoring these changes.

Purpose of the Study:

  • To evaluate the predictive power of hyperspectral data for estimating heavy metal concentrations in grapevine leaves.
  • To identify sensitive spectral regions for detecting copper (Cu), zinc (Zn), lead (Pb), chromium (Cr), and cadmium (Cd) stress.

Main Methods:

  • Grapevine seedlings were subjected to five different heavy metal stress treatments (Cu, Zn, Pb, Cr, Cd).
  • Hyperspectral data (350-2500 nm) and heavy metal content were collected from leaves.
  • Partial Least Squares (PLS) for feature selection, and Multiple Linear Regression (MLR) and Support Vector Machine (SVM) regression for modeling were employed.

Main Results:

  • Specific wavelengths in the visible and red-edge regions were identified as most sensitive for estimating individual heavy metal concentrations.
  • Support Vector Machine (SVM) regression models achieved high prediction accuracy (R² values ranging from 0.56 to 0.86) for Cu, Zn, Pb, Cr, and Cd.
  • Significant alterations in spectral patterns of stressed grapevines compared to healthy samples were observed, confirming heavy metal impact.

Conclusions:

  • In-field spectroscopy is an efficient technique for quantifying heavy metal content in grapevine foliage.
  • The study demonstrates the potential of spectroscopy for early detection of heavy metal stress in agricultural settings.
  • This approach contributes to safeguarding human health through improved monitoring of food-producing ecosystems.