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Related Experiment Video

Updated: Jul 23, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
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Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset.

Dominik Rößle1, Lukas Prey2, Ludwig Ramgraber3

  • 1AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany.

Plant Phenomics (Washington, D.C.)
|July 17, 2023
PubMed
Summary
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The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations.

Sensors (Basel, Switzerland)·2023

A new noninvasive model uses RGB images to classify Fusarium head blight (FHB) severity in wheat, offering a faster, cheaper alternative to traditional methods. This automated system aids in accelerating disease resistance breeding.

Area of Science:

  • Agricultural Science
  • Plant Pathology
  • Computer Vision

Background:

  • Fusarium head blight (FHB) is a major wheat disease causing significant yield losses and health concerns.
  • Current FHB phenotyping methods are costly and labor-intensive, hindering rapid breeding efforts.
  • Objective and efficient FHB severity assessment is critical for developing resistant wheat varieties.

Purpose of the Study:

  • To develop a noninvasive classification model for estimating Fusarium head blight (FHB) severity using RGB images.
  • To create a novel, large-scale dataset for FHB severity assessment.
  • To evaluate the model's performance and generalizability across different years.

Main Methods:

  • Utilized a pretrained EfficientNet (b0) model, adapted as a regression model for FHB severity classification.

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  • Developed a classification model requiring minimal image preprocessing, accepting images from low-cost RGB cameras.
  • Introduced a dataset comprising approximately 3,000 images from 2020-2022 with dual-rater severity assessments.

    • Main Results:

    • The model achieved high individual network-to-rater reliability, surpassing interrater reliability.
    • Demonstrated significant generalization capabilities when trained on multi-year data and tested on independent data.
    • Multi-year training improved performance metrics (e.g., accuracy, kappa) and reduced error compared to single-year training.

    Conclusions:

    • The proposed lightweight model offers an automated, objective, and cost-effective solution for FHB severity assessment.
    • The model's ability to generalize across years is crucial for robust field deployment.
    • Potential for deployment on mobile devices, enabling widespread and accessible FHB phenotyping.