Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

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

Immunoproteasome inhibition protects the retina from Alzheimer's-associated degeneration.

BMB reports·2026

Same author

Metformin Activates AMP-Activated Protein Kinase-Transcription Factor EB Signaling to Restore Lysosomal and Mitochondrial Homeostasis and Suppress Epithelial-to-Mesenchymal Transition in Oxidative Stress-Injured Retinal Pigment Epithelium.

The American journal of pathology·2026

Same author

Exploring morphological traits related to potential milling yield based on image-analysis.

Scientific reports·2026

Same author

High-throughput phenomic survey of seed morphological diversity in common and tartary buckwheat germplasm.

Scientific reports·2026

Same author

Evaluating phenotypic plasticity of reproductive traits among Korean rice cultivars under diverse climatic conditions.

Frontiers in plant science·2026

Same author

Illumination Removal for Accurate Skin Color Extraction Using Dermoscopy and Transformer-Based Modeling.

Annals of dermatology·2026

Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026

Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026

Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026

Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026

Same journal

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 journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 24, 2025

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

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

Published on: March 28, 2025

Python algorithm package for automated Estimation of major legume root traits using two dimensional images.

Amit Ghimire^1,2, Yong Suk Chung³, Sungmoon Jeong^4,5

¹Department of Applied Biosciences, Kyungpook National University, Daegu, 41566, Republic of Korea.

Scientific Reports

|March 2, 2025

Summary

This summary is machine-generated.

A new Python algorithm accurately estimates legume root traits like total root length and surface area from 2D images. This open-source tool offers a cost-effective, reliable alternative to expensive software for plant research.

Keywords:

Image processing Legumes Python algorithm Root traits Threshold

More Related Videos

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

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

Published on: August 8, 2017

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

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

Published on: January 25, 2014

Related Experiment Videos

Last Updated: May 24, 2025

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

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

Published on: March 28, 2025

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

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

Published on: August 8, 2017

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

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

Published on: January 25, 2014

Area of Science:

Agricultural Science
Computer Science
Plant Biology

Background:

Accurate root trait analysis is crucial for understanding plant growth and development.
Existing software for root trait estimation can be expensive and inaccessible.

Purpose of the Study:

To develop and validate a simple, open-source Python algorithm for estimating key legume root traits.
To compare the algorithm's performance against established software and ground truth data.

Main Methods:

Utilized a Python algorithm with four thresholding methods (Otsu, Gaussian adaptive, mean adaptive, triangle) for image analysis.
Employed distance transform and ConnectedComponentsWithStat functions for trait estimation.
Validated results against WinRHIZO, RhizoVision, and ground truth data for 400 legume root images.

Main Results:

The Python algorithm demonstrated high accuracy, with R² ≥0.98 compared to ground truth data for all traits.
Otsu thresholding was optimal for distance transform methods, while triangle thresholding excelled for total root length.
Error metrics (RMSE, MBE) were minimal, indicating strong agreement with ground truth and WinRHIZO software.

Conclusions:

The developed Python algorithm provides a simple, cost-effective, and accurate method for legume root trait estimation.
The open-source nature allows for modification and adaptation by researchers.
This tool can significantly benefit plant science research by democratizing root image analysis.