Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Stress: General Loading Conditions

Stress: General Loading Conditions

To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses.

Plants (Basel, Switzerland)·2026

Same author

Development of a deep neural network model for simultaneous analysis of extracellular analyte gradients for a population of cells.

Artificial intelligence in the life sciences·2026

Same author

Editorial: Agricultural innovation in the age of climate change: a 4.0 approach.

Frontiers in plant science·2025

Same author

Programming-Assisted Imaging of Cellular Nitric Oxide Efflux Gradients and Directionality via Carbon Nanotube Sensors.

Small science·2025

Same author

Global Greenhouse Gas Emissions From Agriculture: Pathways to Sustainable Reductions.

Global change biology·2024

Same author

Global Land Use Change and Its Impact on Greenhouse Gas Emissions.

Global change biology·2024

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.

Sruti Das Choudhury^1,2, Carmela Rosaria Guadagno³, Srinidhi Bashyam²

¹School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States.

Frontiers in Plant Science

|May 6, 2024

Summary

This summary is machine-generated.

Autofluorescence imaging identified drought stress in Brassica rapa genotypes. Oilseed types showed higher stress tolerance than other genotypes, advancing plant phenotyping research.

Keywords:

Brassica rapa autofluorescence imaging drought stress genotypic variation high throughput plant phenotyping machine learning-based classifier stress detection

More Related Videos

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

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

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

Published on: March 28, 2025

Related Experiment Videos

Last Updated: Jun 29, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

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

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

Published on: March 28, 2025

Area of Science:

Plant Science
Biophysics
Genetics

Background:

Autofluorescence imaging offers a non-destructive method to assess plant biochemical and physiological traits.
Genotypic variations in plants can be characterized by their optical properties.
Advancements in autofluorescence-based plant phenotyping are crucial for understanding plant stress responses.

Purpose of the Study:

To compare stress-tolerant and stress-susceptible Brassica rapa genotypes using autofluorescence imaging.
To introduce and analyze novel stress-based phenotypes derived from autofluorescence data.
To apply machine learning techniques for enhanced plant stress detection and characterization.

Main Methods:

Autofluorescence spectral images were analyzed using machine learning algorithms to classify stressed and non-stressed plant tissues.
Time-series image sequences from three Brassica rapa genotypes (CC, R500, VT) were captured at a high-throughput plant phenotyping facility.
Two novel stress-based image phenotypes, average percentage stress and moving average percentage stress, were computed to quantify temporal stress variations.

Main Results:

The computed phenotypes effectively distinguished between stressed and non-stressed tissues across different genotypes.
The oilseed genotype (R500) exhibited greater tolerance to drought stress compared to genotypes CC and VT.
Autofluorescence signals at 365/400 nm excitation/emission successfully segregated genotypic variations under drought stress.

Conclusions:

Autofluorescence imaging combined with machine learning provides a powerful tool for plant stress phenotyping.
Genotypic differences in drought stress response were successfully identified using autofluorescence spectral imaging.
This approach enables the exploration of novel phenotypes for significant applications in plant science and breeding.