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

You might also read

Related Articles

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

Sort by
Same author

Translating functional molecular knowledge into crop-breeding success.

Nature reviews. Genetics·2026
Same author

Correction: DeltaBreed: A BrAPI-centric breeding data information system.

PloS one·2026
Same author

GrainGenes: genetics, genomes, and pangenomes.

Genetics·2025
Same author

DeltaBreed: A BrAPI-centric breeding data information system.

PloS one·2025
Same author

Author Correction: A pangenome and pantranscriptome of hexaploid oat.

Nature·2025
Same author

Global genomic population structure of wild and cultivated oat reveals signatures of chromosome rearrangements.

Nature communications·2025

Related Experiment Video

Updated: Dec 21, 2025

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems
09:21

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

Published on: August 17, 2022

1.5K

Improving root characterisation for genomic prediction in cassava.

Bilan Omar Yonis1, Dunia Pino Del Carpio2,3, Marnin Wolfe2

  • 1Montpellier SupAgro, 34060, Montpellier, Cedex, 02, France.

Scientific Reports
|May 16, 2020
PubMed
Summary

Image analysis of cassava storage roots identified new quantitative trait loci (QTL) for root shape and size. This high-throughput phenotyping approach aids breeding for improved uniformity and traits.

More Related Videos

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients
07:45

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Published on: October 22, 2018

16.6K
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.8K

Related Experiment Videos

Last Updated: Dec 21, 2025

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems
09:21

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

Published on: August 17, 2022

1.5K
An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients
07:45

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Published on: October 22, 2018

16.6K
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.8K

Area of Science:

  • Agricultural Science
  • Genetics
  • Plant Breeding

Background:

  • Cassava's drought tolerance and carbohydrate-rich roots are vital, but irregular storage root shapes hinder processing.
  • Improving cassava storage root uniformity is crucial for efficient harvesting and post-harvest applications.

Purpose of the Study:

  • To evaluate image phenotyping feasibility for cassava storage root traits.
  • To assess genome-wide association studies (GWAS) and genomic prediction for image-extracted size and shape traits.
  • To identify quantitative trait loci (QTL) associated with root size and shape.

Main Methods:

  • Phenotyped cassava Genetic gain and offspring (C1) populations using field image analysis of storage roots.
  • Conducted genome-wide association analysis (GWAS) to detect QTL for shape and size traits.
  • Calculated standard deviation (SD) of root measurements to identify QTL for uniformity-related traits.

Main Results:

  • Detected significant QTL for root size and shape on chromosomes 1 and 12.
  • Identified new QTL for Perimeter, Feret, and Aspect Ratio on chromosomes 6, 9, and 16 using SD measurements.
  • Achieved higher predictive accuracies for image-extracted root size and shape traits compared to yield traits.

Conclusions:

  • Image-based phenotyping is a promising high-throughput method for cassava breeding.
  • GWAS and genomic prediction are effective for selecting desirable storage root size and shape traits.
  • This approach can accelerate breeding programs focused on root uniformity and quality.