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

In-context adaptation of VLMs for few-shot cell detection in optical microscopy.

Frontiers in artificial intelligence·2026
Same author

Steroid-responsive delayed multifocal encephalopathy following vasculotoxic snakebite with serial MRI evolution: a case report.

Toxicon : official journal of the International Society on Toxinology·2026
Same author

DeCAF: Decentralized consensus-and-factorization for low-rank adaptation of foundation models.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Image-based high-throughput phenotyping enables genetic analyses of pod morphological traits in mungbean (Vigna radiata (L.) R. Wilczek).

G3 (Bethesda, Md.)·2026
Same author

MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.

Plant phenomics (Washington, D.C.)·2026
Same author

Classifying Cognitive Decline in Older Drivers from Behavior on Adverse Roads Detected Using Computer Vision.

Journal of transportation technologies·2026
Same journal

Leveraging target enrichment and genome skimming (Hyb-Seq) of herbarium collections to unlock timber DNA barcoding.

Applications in plant sciences·2026
Same journal

Detecting cryptic ghost lineage introgression in four-taxon genomic datasets.

Applications in plant sciences·2026
Same journal

HapAsmbl: A reference-aided pipeline for assembling haplotypes in Nanopore amplicon sequence data of polymorphic populations.

Applications in plant sciences·2026
Same journal

HybSuite: An integrated pipeline for hybrid capture phylogenomics from reads to trees.

Applications in plant sciences·2026
Same journal

Detecting introgression from phylogenetic invariant site patterns using machine learning.

Applications in plant sciences·2026
Same journal

tanggle: An R package for the visualization of phylogenetic networks.

Applications in plant sciences·2026
See all related articles

Related Experiment Video

Updated: Dec 12, 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

9.7K

Automated trichome counting in soybean using advanced image-processing techniques.

Seyed Vahid Mirnezami1,2, Therin Young1, Teshale Assefa3

  • 1Department of Mechanical Engineering Iowa State University Ames Iowa USA.

Applications in Plant Sciences
|August 9, 2020
PubMed
Summary
This summary is machine-generated.

Automated trichome counting in plants is challenging. A new semi-automated method combining image processing and human input achieves nearly 90% accuracy for quantifying plant trichomes.

Keywords:
image processingimaginginsect feedingsoybeantrichome

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

1.4K
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

12.7K

Related Experiment Videos

Last Updated: Dec 12, 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

9.7K
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

1.4K
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

12.7K

Area of Science:

  • Plant Biology
  • Agricultural Science
  • Image Analysis

Background:

  • Trichomes are plant epidermal outgrowths crucial for defense against herbivores.
  • Accurate trichome quantification is vital for understanding plant defense mechanisms and responses to insect infestation.
  • Existing automated methods face challenges due to trichome variability and occlusion.

Purpose of the Study:

  • To develop and validate a simplified, accurate method for automated and semi-automated trichome counting.
  • To address challenges in trichome quantification, including occlusion and coloration variability.
  • To improve the efficiency of trichome analysis in plant research.

Main Methods:

  • Developed heuristic image-processing techniques, including thresholding and graph-based algorithms.
  • Applied and compared two automated and two semi-automated methods for trichome counting.
  • Utilized regression analysis to evaluate method performance using soybean (Glycine max) leaves.

Main Results:

  • A semi-automated method, the manually annotated trichome intersection curve, demonstrated the highest performance.
  • This method achieved an accuracy close to 90% when compared to manual counts.
  • The study successfully quantified trichomes across 10 soybean genotypes with varying trichome abundance.

Conclusions:

  • A novel semi-automated approach effectively quantifies plant trichomes by integrating image processing with human intervention.
  • This method overcomes key challenges like trichome occlusion, enabling rapid and accurate trichome identification.
  • The developed technique offers significant potential for advancing research in plant science and related fields.