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

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.

You might also read

Related Articles

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

Sort by
Same author

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026
Same author

Exogenous Melatonin Alleviates NaCl-Induced Salinity Stress in Forage Pea (<i>Pisum sativum</i> L.): Concentration Optimization and Genotype-Specific Responses.

Metabolites·2026
Same author

Application of NGS Technology, Association Mapping, and Physical Mapping Technologies to Identify Candidate Genes Associated with Maize (<i>Zea mays</i> L.) Hybrid Yield.

International journal of molecular sciences·2026
Same author

Integrating AMMI, Bayesian-AMMI, BLUP, and GGE biplot models to improve the identification of high-yielding and stable barley genotypes.

BMC plant biology·2026
Same author

Biocontrol of <i>Fusarium</i> and Other Fungal Diseases of Cereals Using Bacterial Compounds and Plant Extracts.

Molecules (Basel, Switzerland)·2026
Same author

Ultrastructural changes and defense strategy of yellow lupine during silver nanoparticle and Fusarium oxysporum interaction.

Scientific reports·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 12, 2026

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

9.8K

Monitoring wheat leaf rust severity using machine learning techniques.

Tayebeh Bakhshi1, Rahim Mehrabi2, Mostafa Aghaee Sarbarzeh3

  • 1Department of Crop Biotechnology and Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad, P.O. Box 891779489974, Mashhad, Iran.

Scientific Reports
|November 29, 2025
PubMed
Summary
This summary is machine-generated.

Wheat leaf rust resistance varies among Iranian genotypes. Some wheat varieties show strong resistance to all tested Puccinia triticina isolates, aiding disease management strategies.

Keywords:
Leaf rustResistance geneVirulence factorWheat

More Related Videos

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone
07:36

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

2.1K
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

1.5K

Related Experiment Videos

Last Updated: May 12, 2026

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

9.8K
Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone
07:36

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

2.1K
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

1.5K

Area of Science:

  • Plant Pathology
  • Agronomy
  • Genetics

Background:

  • Wheat leaf rust (Puccinia triticina) causes significant global yield losses.
  • Understanding regional pathogen virulence is crucial for effective wheat breeding.

Purpose of the Study:

  • To evaluate pathogenic factors of Iranian leaf rust isolates.
  • To identify resistant wheat genotypes and characterize resistance genes.

Main Methods:

  • Assessed infection types on 49 durum and bread wheat genotypes against 9 leaf rust isolates.
  • Determined virulence/avirulence patterns on differential genotypes with known resistance genes (Lr34, Lr37, Lr19).
  • Utilized machine learning algorithms (XGBoost, MARS, GP) to model disease impact on yield traits.

Main Results:

  • Significant differences in wheat genotype responses to isolates were observed.
  • Several genotypes (e.g., P.S. No4, Shabrang) showed resistance to all isolates.
  • All isolates were virulent on Lr34 and Lr37, but avirulent on Lr19.

Conclusions:

  • Identified specific wheat genotypes with valuable seedling resistance genes.
  • Characterized virulence profiles of Puccinia triticina isolates in Iran.
  • Machine learning models accurately predicted yield losses due to disease severity.