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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

You might also read

Related Articles

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

Sort by
Same author

SLP-Net: A Dual-Level Contrastive Learning Framework with Stripe Attention for Elongated Pepper Detection in Complex Field Environments.

Plants (Basel, Switzerland)·2026
Same author

Species-specific tree structural parameters extraction via UAV RGB-LiDAR data and multimodal instance segmentation.

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

Transferability of aboveground biomass estimation using Sentinel-1/2 and GEDI data in subtropical forests of complex terrain, China.

iScience·2026
Same author

Rethinking the lipid paradox: the role of baseline characteristics in LDL-C and long-term mortality after acute myocardial infarction.

Lipids in health and disease·2026
Same author

Advances in Artificial Intelligence for Plant Research.

Plants (Basel, Switzerland)·2026
Same author

Fitting maximum crown width height of Chinese fir through ensemble learning combined with fine spatial competition.

Plant phenomics (Washington, D.C.)·2025
Same journal

Deep learning-based 3D morphological segmentation and quantitative growth analysis of field-grown cabbage across the full cycle.

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

Phenotyping seasonal photosynthetic energy-partitioning states in temperate evergreen conifers using hyperspectral imaging.

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

Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomes.

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

Hyperspectral imaging reveals early drought stress and associated molecular responses in lettuce for space agriculture.

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

Transpiration responds linearly to Penman-Monteith reference evapotranspiration and varies genetically, both in individual plants and canopies, in large sorghum and pearl millet panels.

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

Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation.

Plant phenomics (Washington, D.C.)·2026
See all related articles

Related Experiment Video

Updated: Jun 9, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Multimodal collaborative UAV framework for single rubber tree parsing.

Weiqi Yin1, Jie Zhang1, Hengrui Wang1

  • 1Central South University of Forestry and Technology, Changsha, Hunan, 410004, China.

Plant Phenomics (Washington, D.C.)
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

We developed MDA-SegNet, a novel framework for accurate individual-tree segmentation in dense rubber plantations. This method improves resource inventory and agro-forestry management by effectively separating overlapping tree crowns.

Keywords:
Individual-tree segmentationLiDAR point cloudMultimodal fusionRubber tree

Related Experiment Videos

Last Updated: Jun 9, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Forestry Science
  • Computer Vision
  • Remote Sensing

Background:

  • Accurate individual-tree segmentation is crucial for rubber plantation management.
  • Challenges include overlapping crowns, varying point density, and diverse tree morphology.
  • Existing methods struggle with the complexities of densely planted stands.

Purpose of the Study:

  • To introduce MDA-SegNet, a multimodal point-cloud instance segmentation framework.
  • To enhance individual-tree segmentation in densely planted rubber plantations.
  • To improve resource inventory and agro-forestry management.

Main Methods:

  • MDA-SegNet integrates orthophoto and LiDAR data using a Multimodal Deformable Encoding (MDE) module.
  • A Z-Order Selective Mamba (ZOS-Mamba) module addresses vertical dependencies and uneven point density.
  • An Adaptive Lemming Optimization Clustering (ALOC) module refines segmentation for overlapping canopies.

Main Results:

  • MDA-SegNet demonstrated superior performance on a custom rubber tree dataset (RT-Set) and public forest datasets.
  • Achieved an F-score of 87.32% and Recall of 82.90% on RT-Set, outperforming state-of-the-art models.
  • Showcased strong robustness and cross-domain generalizability with higher F-score and mIoU.

Conclusions:

  • MDA-SegNet provides reliable individual-tree segmentation for dense rubber plantations.
  • The framework effectively handles challenges like crown overlap and morphological variations.
  • Offers significant improvements for precision agriculture and forest management applications.