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

A technique for measuring non-structural carbohydrate reserves in flag leaves of paddy rice using Fourier transform infrared spectroscopy (FTIR).

Plant methods·2025
Same author

Ferrihydrite level in paddy soil affects inorganic arsenic species in rice grains.

Environmental science. Processes & impacts·2025
Same author

Hyperspectral imaging to characterize the vegetative tissue biochemical changes in response to water deficit conditions in sorghum (<i>Sorghum bicolor</i>).

Frontiers in plant science·2025
Same author

Transcriptome enhanced rice grain metabolic model identifies histidine level as a marker for grain chalkiness.

Scientific reports·2025
Same author

Reconstruction of a Chronic, Retracted Proximal Hamstring Rupture Using Achilles Tendon Allograft.

Video journal of sports medicine·2025
Same author

Phenotypic and transcriptomic responses of diverse rice accessions to transient heat stress during early grain development.

Frontiers in plant science·2024

Related Experiment Video

Updated: Mar 21, 2026

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

1.8K

Image Harvest: an open-source platform for high-throughput plant image processing and analysis.

Avi C Knecht1, Malachy T Campbell2, Adam Caprez1

  • 1University of Nebraska-Lincoln, Holland Computing Center, Lincoln, NE 68583, USA.

Journal of Experimental Botany
|May 5, 2016
PubMed
Summary

Image Harvest (IH) is a new open-source software framework designed for analyzing large plant phenomics image datasets. It enables efficient, parallel processing and metadata extraction, aiding in crop genetic trait discovery.

Keywords:
High throughput computingOpen Science GridOpenCVimage analysisimage processinglarge-scale biologyopen-source softwarephenomics.

More Related Videos

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response
08:25

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Published on: January 25, 2014

12.9K
Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
06:28

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

2.9K

Related Experiment Videos

Last Updated: Mar 21, 2026

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

1.8K
Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response
08:25

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Published on: January 25, 2014

12.9K
Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
06:28

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

2.9K

Area of Science:

  • Plant Biology
  • Genetics
  • Bioinformatics

Background:

  • High-throughput plant phenotyping generates massive image datasets, posing computational challenges for analysis.
  • Existing image analysis pipelines create bottlenecks, hindering efficient genotype-to-phenotype gap bridging in crops.

Purpose of the Study:

  • To introduce Image Harvest (IH), an open-source, flexible image-analysis framework for high-throughput plant phenotyping.
  • To provide researchers with a scalable solution for processing large-scale image data and extracting digital traits.
  • To demonstrate the utility of IH in identifying genetic loci associated with plant architecture.

Main Methods:

  • Developed Image Harvest (IH) for parallel processing on computing grids with integrated metadata extraction.
  • Integrated IH with the Open Science Grid for cost-free computational resources.
  • Applied IH to a rice (Oryza sativa) diversity panel, extracting digital traits for genome-wide association mapping.

Main Results:

  • Identified three major quantitative trait loci (QTLs) on rice chromosomes 4 and 6 using digital traits derived from IH.
  • These identified QTLs co-localize with known QTLs regulating agronomically important traits in rice.
  • Demonstrated the capability of IH to interpret plant architecture and facilitate genetic discovery.

Conclusions:

  • Image Harvest (IH) offers an efficient, scalable, and accessible solution for analyzing large phenomics image datasets.
  • The software requires a minimal learning curve, empowering plant biologists to analyze complex datasets.
  • IH facilitates the discovery of genetic underpinnings of plant architecture and agronomic traits.