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Related Concept Videos

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Updated: Feb 11, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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An explainable deep machine vision framework for plant stress phenotyping.

Sambuddha Ghosal1, David Blystone2, Asheesh K Singh2

  • 1Department of Mechanical Engineering, Iowa State University, Ames, IA 50011.

Proceedings of the National Academy of Sciences of the United States of America
|April 19, 2018
PubMed
Summary
This summary is machine-generated.

A new machine learning framework accurately identifies and classifies soybean foliar stresses using visual data. This explainable AI provides quantitative stress severity, improving crop management and research.

Keywords:
explainable deep learningmachine learningplant stress phenotypingprecision agricultureresolving rater variabilities

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Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
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Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
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Area of Science:

  • Plant Pathology
  • Agricultural Science
  • Computer Science

Background:

  • Current crop stress identification relies on subjective visual assessment, leading to inaccuracies and resource waste.
  • Inter- and intra-rater variability in visual analysis hinders reliable stress identification, classification, and quantification.
  • Developing objective, automated methods is crucial for efficient crop management and research.

Purpose of the Study:

  • To develop and validate a machine learning (ML) framework for accurate identification, classification, and quantification of soybean foliar stresses.
  • To implement an explainable AI mechanism for visualizing the visual symptoms used in ML predictions.
  • To provide a quantitative measure of stress severity without expert annotation.

Main Methods:

  • Utilized a machine learning framework trained on over 25,000 images of soybean foliar stress.
  • Employed an explanation mechanism using top-K high-resolution feature maps to identify visual symptoms.
  • Validated the model's accuracy in identifying and classifying biotic (bacterial, fungal) and abiotic (chemical, nutrient) stresses.
  • Assessed model robustness to image perturbations and potential for transfer learning across species.

Main Results:

  • Achieved remarkable accuracy in identifying and classifying diverse biotic and abiotic foliar stresses in soybean.
  • The framework provides unsupervised identification of visual symptoms, enabling quantitative stress severity assessment.
  • Demonstrated robustness to input image variations, indicating high-throughput deployment viability.
  • Observed potential for transfer learning, suggesting species-agnostic applicability.

Conclusions:

  • The developed ML framework offers a consistent, rapid, and accurate solution for foliar stress analysis in soybean.
  • Explainable AI features enhance transparency and trust in automated stress detection.
  • The model's potential for deployment on mobile platforms (UAVs, mobile apps) can revolutionize large-scale scouting and real-time farmer support.
  • This technology has significant implications for scientific research, plant breeding, and crop production efficiency.