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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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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Automated, high-throughput hyperspectral imaging enables early detection of grapevine downy mildew and monitoring of vineyard spray program performance.

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Related Experiment Video

Updated: May 21, 2026

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato
06:28

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

Published on: June 7, 2024

Enhancing plant pathology discovery and application development through automated, high-throughput hyperspectral

Saeed Hosseinzadeh1, Dani Martinez2, Rye Henry Weber3

  • 1Cornell University, PPPMB, Geneva, New York, United States; sh2387@cornell.edu.

Plant Disease
|May 20, 2026
PubMed
Summary

Automated high-throughput hyperspectral imaging (AHHI) accelerates plant pathology research by enabling rapid, large-scale data collection. This technology bridges the gap between lab discovery and practical application in disease detection and breeding.

Keywords:
Causal AgentCrop TypeFruitOomycetesSubject AreasTechniquessmall fruits

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Related Experiment Videos

Last Updated: May 21, 2026

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato
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Published on: June 7, 2024

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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Area of Science:

  • Plant pathology
  • Spectroscopy
  • Agricultural science

Background:

  • Hyperspectral sensing offers powerful insights in plant pathology.
  • Current data collection methods face bottlenecks in throughput, data handling, and volume.
  • These limitations hinder the transition of hyperspectral technology from research to practical applications.

Purpose of the Study:

  • To develop an automated high-throughput hyperspectral imaging (AHHI) platform.
  • To overcome limitations in current hyperspectral data collection for plant pathology.
  • To enable scalable, high-volume data acquisition for advanced applications.

Main Methods:

  • An automated system integrating a push broom hyperspectral camera (400-1000 nm) and robotic sample positioning.
  • Acquisition of line images at 100 frames per second, processing large data volumes (2.5 GB per sample, 9 TB per day).
  • Demonstration of use cases including pre-symptomatic disease detection, fungicide detection, and grapevine lineage discrimination.

Main Results:

  • The AHHI platform successfully collected high-quality hyperspectral images at unprecedented scale (9 TB/day).
  • PERMANOVA and Random Forest analyses showed high accuracy (AUC 77.8-99.9%) for disease detection and lineage discrimination.
  • Results mirrored handheld spectrometer accuracies, demonstrating the system's efficacy despite spectral resolution differences.

Conclusions:

  • The AHHI platform significantly enhances throughput and data volume for hyperspectral plant pathology studies.
  • This technology facilitates the translation of hyperspectral discoveries from niche research to widespread practical use.
  • AHHI accelerates the adoption of advanced hyperspectral applications in agriculture and plant science.