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Related Concept Videos

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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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UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given...
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Updated: Sep 19, 2025

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton
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A dataset for vineyard disease detection via multispectral imaging.

Alessio Saccuti1, Filippo Graziosi2, Dario Lodi Rizzini1

  • 1University of Parma, Parco Area delle Scienze 181/A, 43100 Parma, Italy.

Data in Brief
|June 19, 2025
PubMed
Summary

This dataset provides multispectral images for detecting grapevine diseases like Flavescence dorée (FD) and Esca (ED). It supports developing precision agriculture tools for early disease identification in vineyards.

Keywords:
Flavescence DoréeMal d’EscaMultispectral imagingPrecision agricultureVineyard diseases

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Computer Vision

Background:

  • Grapevine diseases Flavescence dorée (FD) and Esca (ED) pose significant threats to viticulture.
  • Limited public datasets exist for multispectral analysis of these grapevine diseases, especially field-collected data.

Purpose of the Study:

  • To introduce a novel multispectral image dataset for the development of grapevine disease detection algorithms.
  • To facilitate research in automated disease identification and precision agriculture.

Main Methods:

  • Collected multispectral images using a Micasense RedEdge-P camera (six spectral bands).
  • Captured 172 images from a frontal perspective of three grapevine varieties (Ancellotta, Marani, Salamino).
  • Included raw/processed images, camera calibration data, and health annotations.

Main Results:

  • A comprehensive dataset comprising raw and processed multispectral images.
  • Detailed annotations on plant health conditions for machine learning model training.
  • Provided Python-based examples for dataset utilization.

Conclusions:

  • The dataset is a valuable resource for advancing automated detection of grapevine diseases using multispectral imaging.
  • Enables further research in machine learning, image processing, and precision agriculture techniques.