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

Improving pear fruit quality without yield loss through 3D point cloud-based estimation of reasonable fruit load.

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

Same author

Structural parameter determination and pruning pattern analysis of pear tree shoots for dormant pruning.

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

Same author

Far-red light in early growth stages boosts lettuce biomass and preserves anthocyanins.

Annals of botany·2026

Same author

Photochemical formation process of ozone and spatiotemporally targeted control strategies along the transport pathways in the Pearl River Delta region.

Journal of environmental sciences (China)·2025

Same author

A Connected Building Landscape dataset for Instance Segmentation.

Scientific data·2025

Same author

Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset.

Sensors (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Jul 30, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Automatic Branch-Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional

Haitao Li^1,2, Gengchen Wu¹, Shutian Tao³

¹Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This study introduces an automated pipeline using lidar point clouds to measure pear leaf traits like angle, length, and area. This non-destructive method accurately monitors plant growth and optimizes orchard management.

Keywords:

leaf phenotype pear canopy point cloud segmentation

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

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Published on: October 25, 2024

Related Experiment Videos

Last Updated: Jul 30, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

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

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Published on: October 25, 2024

Area of Science:

Plant Science and Agricultural Technology
Computer Vision and Machine Learning in Botany

Background:

Leaf phenotypic traits are crucial for canopy photosynthesis efficiency.
Traditional destructive sampling and manual measurements are time-consuming and hinder continuous plant growth monitoring.
Non-destructive, accurate measurement of leaf traits is needed for efficient plant study.

Purpose of the Study:

To propose an automatic branch-leaf segmentation pipeline using lidar point clouds.
To conduct automatic measurements of leaf inclination angle, length, width, and area.
To demonstrate the method's significance for monitoring pear tree growth and orchard management.

Main Methods:

Established a 3D canopy model using lidar point cloud data via SCENE software.
Employed the PointNet++ model for semantic segmentation of branches and leaves from point cloud data.
Utilized a mean shift clustering algorithm for single leaf instance extraction and calculated leaf parameters via plane fitting and triangulation.

Main Results:

Achieved high accuracy in semantic segmentation (mean IoU of 0.88) and leaf instance extraction (mCoV of 0.87).
Demonstrated strong correlations between automatically measured and manually measured leaf parameters (correlation coefficients ranging from 0.91 to 0.94).
Quantified leaf inclination, length, width, and area with high precision, showing minimal root mean squared errors.

Conclusions:

The proposed lidar-based pipeline enables automatic and accurate measurement of pear leaf phenotypic parameters.
This non-destructive approach significantly aids in continuous monitoring of plant growth and simulation of canopy photosynthesis.
The method offers substantial benefits for optimizing orchard management and agricultural practices.