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.

Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

You might also read

Related Articles

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

Sort by

Same author

Gender discrimination, marital attitude, and perceived choice and awareness as explanatory factors of flourishing among young Indian unmarried women.

Discover mental health·2026

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

Association between household fuel use and cardiometabolic risk factors in sub-Saharan Africa: A systematic review.

Environmental pollution (Barking, Essex : 1987)·2026

Same author

Single-Center Experience with the Optiblock Coil: An Efficient and Thrombogenic Solution for Targeted Vessel Takedown.

World neurosurgery·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 journal

PlasmiDB: an open-source and customizable database for plasmid lifecycle management in multi-user, multi-project plant molecular biology laboratories.

Plant methods·2026

Same journal

Establishment of a protoplast isolation and transient transformation system for tung tree (Vernicia fordii).

Plant methods·2026

Same journal

Deep aerenchyma: a transformer-based pipeline for scalable phenotyping of rice root aerenchyma lacunae across environments.

Plant methods·2026

Same journal

Comparative analysis of SP3 and S-Trap sample preparation protocols for proteomic profiling associated with somatic embryogenesis efficiency in Olea europaea L.

Plant methods·2026

Same journal

DAPR-AM-Net: an end-to-end smart farming system powered by dual-attention progressive refinement and adaptive MixUp for explainable tomato leaf disease classification and forecasting.

Plant methods·2026

Same journal

Deep soil layers show the most pronounced genetic variation in wheat root length.

Plant methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 29, 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

Computer vision and machine learning enabled soybean root phenotyping pipeline.

Kevin G Falk¹, Talukder Z Jubery², Seyed V Mirnezami²

¹1Department of Agronomy, Iowa State University, Ames, USA.

|January 30, 2020

Summary

This summary is machine-generated.

A new, low-cost root phenotyping system uses computer vision and machine learning to efficiently measure root system architecture (RSA) traits. This high-throughput method reveals genetic variability in RSA, aiding crop breeding for enhanced resilience.

Keywords:

Breeding Computer vision Image analysis Machine learning Phenomics Phenotyping RSA Root Soybean Time series

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: Dec 29, 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:

Plant Science
Genetics
Agricultural Engineering

Background:

Root system architecture (RSA) traits are crucial for crop breeding but difficult to measure accurately.
Traditional methods are resource-intensive and yield variable results.
Advancements in computer vision and machine learning (ML) offer new possibilities for RSA trait analysis.

Purpose of the Study:

To develop a mobile, low-cost, high-resolution root phenotyping system.
To establish an end-to-end pipeline for image-based root trait processing and analysis.
To assess genetic variability in RSA traits within diverse soybean accessions.

Main Methods:

A high-throughput phenotyping system integrating time-series image capture and automated processing.
Use of optical character recognition (OCR) for seedling identification and convolutional auto-encoder (CAE) for image segmentation.
Customized Automatic Root Imaging Analysis (ARIA) software for feature extraction.

Main Results:

The system successfully processed large quantities of root samples.
Significant genetic variability was observed for various RSA traits, including root shape, length, number, mass, and angle.
The system demonstrated capacity for handling hundreds to thousands of plants.

Conclusions:

The developed system offers a cost-effective, non-destructive, high-throughput method for obtaining time-series root data.
This platform is valuable for phenomics, genomics, and plant breeding, serving as a selection tool.
Image-based root phenotyping is essential to complement shoot phenotyping in breeding programs.