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

Light Acquisition02:16

Light Acquisition

9.1K
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.
9.1K

You might also read

Related Articles

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

Sort by
Same author

Correction to "Controlled Bioactive Delivery Using Degradable Electroactive Polymers".

Biomacromolecules·2025
Same author

Progress in Multiscale Modeling of Silk Materials.

Biomacromolecules·2024
Same author

Poly(2-Hydroxyethyl Methacrylate) Hydrogel-Based Microneedles for Bioactive Release.

Bioengineering (Basel, Switzerland)·2024
Same author

Compact Back-End Electronics with Temperature Compensation and Efficient Data Management for In Situ SiPM-Based Radiation Detection.

Sensors (Basel, Switzerland)·2023
Same author

Collaborative Continuum Robots for Remote Engineering Operations.

Biomimetics (Basel, Switzerland)·2023
Same author

Controlled Bioactive Delivery Using Degradable Electroactive Polymers.

Biomacromolecules·2022

Related Experiment Video

Updated: Dec 4, 2025

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

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

Published on: January 21, 2013

29.1K

Plant Leaf Position Estimation with Computer Vision.

James Beadle1, C James Taylor1, Kirsti Ashworth2

  • 1Engineering Department, Lancaster University, Lancaster LA1 4YW, UK.

Sensors (Basel, Switzerland)
|October 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a computer-vision method for locating plant leaves using a single camera and a robot. The system accurately estimates leaf 3D positions, advancing autonomous plant analysis.

Keywords:
computer visiondepth estimationneural networkparallaxposition estimation

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.9K
Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

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

Published on: August 29, 2019

14.0K

Related Experiment Videos

Last Updated: Dec 4, 2025

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

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

Published on: January 21, 2013

29.1K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.9K
Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

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

Published on: August 29, 2019

14.0K

Area of Science:

  • Robotics and Computer Vision
  • Plant Science and Phenotyping

Background:

  • Accurate plant leaf identification and localization are crucial for autonomous phenotyping and health monitoring.
  • Existing depth sensing methods (infrared, ultrasound) have limitations like ambient light interference or wide fields of view.
  • Stereoscopic or structured light scanners are alternatives but can be costly and complex.

Purpose of the Study:

  • To develop a fully computer-vision based solution for estimating the 3D location of plant leaves.
  • To overcome the limitations of existing depth sensing technologies for plant analysis.
  • To demonstrate a cost-effective and less complex approach using a single camera and robotic positioning.

Main Methods:

  • Utilized a single digital camera autonomously positioned by a three-axis linear robot.
  • Employed a custom-trained neural network for leaf classification across multiple images.
  • Applied parallax calculations to determine leaf depth and subsequently their 3D positions.

Main Results:

  • Successfully demonstrated a proof of concept for the computer-vision based leaf localization method.
  • Initial tests with positioned leaves indicated an expected error of approximately 20 mm.
  • Identified areas for future modifications to enhance accuracy and applicability.

Conclusions:

  • The presented computer-vision approach offers a viable alternative for 3D leaf localization in autonomous plant analysis.
  • The method shows promise for improving the efficiency and accuracy of plant phenotyping and monitoring.
  • Further development is expected to increase the utility of this technique across diverse plant canopies.