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 authorSame journal

Thermal image segmentation in weedy fields via synthetic RGB-trained models and GAN-based cross-modality alignment.

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

Same author

A vision language model for generating XML-based organ-level plant architecture representations of cowpea from simulated images.

Frontiers in artificial intelligence·2026

Same author

Toward Generating Realistic 3D Semantic Training Data for Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Leaf gas exchange measurement for steady-state stomatal conductance model calibration.

The New phytologist·2026

Same author

Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring.

Scientific data·2026

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

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

Generation of labeled leaf point clouds for plants trait estimation.

Gianmarco Roggiolani¹, Brian N Bailey², Jens Behley¹

¹Center for Robotics, University of Bonn, Germany.

Plant Phenomics (Washington, D.C.)

|December 19, 2025

Summary

This study introduces a neural network to generate synthetic 3D leaf point clouds for improved plant phenotyping. This method enhances the accuracy of leaf trait estimation, crucial for understanding crop growth and resistance.

Keywords:

3D plant phenotyping Deep learning for agriculture Leaf trait estimation

More Related Videos

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

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

Related Experiment Videos

Last Updated: Jan 7, 2026

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

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

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

Area of Science:

Plant Science
Computer Vision
Machine Learning

Background:

Leaf trait estimation is traditionally labor-intensive, limiting automated phenotyping accuracy.
Manual measurements are essential for training machine learning models but are time-consuming.
Accurate leaf trait estimation is vital for crop growth, yield, and pest resistance analysis.

Purpose of the Study:

To develop a neural network-based method for generating synthetic 3D leaf point clouds with associated traits.
To support and improve automated plant phenotyping approaches.
To enhance the performance of leaf trait estimation methods.

Main Methods:

A neural network was trained on real-world leaf point clouds to generate realistic synthetic leaves from extracted leaf skeletons.
Generated synthetic leaf data was used to fine-tune various leaf trait estimation models.
The performance of trait estimation methods trained on synthetic data was evaluated against real-world data and other synthetic datasets.

Main Results:

The proposed method generates synthetic leaf point clouds with high similarity to real-world leaves.
Fine-tuning trait estimation methods on the generated synthetic data significantly improved their performance on real-world leaf trait estimation.
The synthetic data proved crucial for developing and testing data-driven trait estimation methods.

Conclusions:

The neural network-based generation of synthetic leaf data offers a viable solution to the limitations of manual data collection in plant phenotyping.
This approach enhances the accuracy and efficiency of leaf trait estimation, contributing to better crop monitoring and management.
The generated synthetic data is a valuable resource for advancing data-driven approaches in plant science research.