Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Light Acquisition02:16

Light Acquisition

8.7K
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.
8.7K

You might also read

Related Articles

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

Sort by
Same author

Transforming cutting-edge healthcare: Emerging trends in metabolomics and drug design using artificial intelligence and big data methodologies.

Digital health·2026
Same author

Intrinsic motivation-based exploration for enhancing tuberculosis lesion discovery in sparse annotation chest X-ray datasets.

The Indian journal of tuberculosis·2025
Same author

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review.

Physics in medicine and biology·2024
Same author

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird.

Scientific reports·2024
Same author

Drought stress detection technique for wheat crop using machine learning.

PeerJ. Computer science·2023
Same author

An enhanced version of Harris Hawks Optimization by dimension learning-based hunting for Breast Cancer Detection.

Scientific reports·2021

Related Experiment Video

Updated: Oct 4, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K

Inferring Agronomical Insights for Wheat Canopy Using Image-Based Curve Fit K-Means Segmentation Algorithm and

Ankita Gupta1, Lakhwinder Kaur1, Gurmeet Kaur2

  • 1Department of Computer Science and Engineering, Punjabi University, Patiala 147002, India.

International Journal of Genomics
|February 10, 2022
PubMed
Summary
This summary is machine-generated.

Phenomics and chlorophyll fluorescence imaging reveal wheat canopy changes under stress. A novel CfitK-means algorithm effectively segments images, identifying 23 texture features crucial for detecting water stress indicators.

More Related Videos

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

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

Published on: March 28, 2025

1.1K
Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
06:21

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

9.0K

Related Experiment Videos

Last Updated: Oct 4, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

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

Published on: March 28, 2025

1.1K
Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
06:21

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

9.0K

Area of Science:

  • Plant science
  • Agronomy
  • Computational biology

Background:

  • Phenomics and chlorophyll fluorescence are key to understanding plant stress responses.
  • Image-based analysis of wheat canopy morphology provides insights into plant health.
  • Chlorophyll fluorescence signals indicate photosynthetic activity and stress levels.

Purpose of the Study:

  • To develop and validate an algorithm for analyzing image-based morphological changes in wheat canopies.
  • To identify water stress indicators in wheat using texture features derived from canopy images.
  • To compare the performance of different image segmentation algorithms for wheat canopy analysis.

Main Methods:

  • A three-stage algorithm involving dynamic thresholding via curve fitting, iterative K-means segmentation (CfitK-means), and computation of 23 GLCM texture features.
  • Statistical analyses including correlation, factor, and agglomerative clustering were employed.
  • A public repository of wheat canopy images with normal and water-stressed chlorophyll fluorescence data was utilized.

Main Results:

  • The CfitK-means algorithm achieved a high IoU score of 95.75%, outperforming seven other segmentation algorithms.
  • All 23 computed GLCM texture features were found to be effective in studying water stress-induced changes in wheat canopy shape and structure.
  • The analyses successfully identified indicators of water stress.

Conclusions:

  • The CfitK-means algorithm is a robust method for segmenting wheat canopy images, crucial for phenomic analysis.
  • GLCM texture features derived from segmented images are valuable for detecting and understanding water stress in wheat.
  • This approach offers significant potential for improving crop monitoring and management strategies.