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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Updated: Aug 11, 2025

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Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization

Yuxing Fu1,2, Yuyang Xia2, Huiming Zhang1,2

  • 1School of Mechanical and Electrical Engineering, Hainan University, Haikou, China.

Frontiers in Plant Science
|February 6, 2023
PubMed
Summary
This summary is machine-generated.

Automated jujube pruning uses skeletonization to identify optimal pruning points. This method improves efficiency and provides a visual basis for robotic pruning systems.

Keywords:
dormant pruningjujube treepruning point identificationskeletonspace colonization algorithm

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Area of Science:

  • Agricultural Engineering
  • Robotics
  • Computer Vision

Background:

  • Jujube pruning is labor-intensive and time-consuming, relying heavily on manual operations.
  • Shortage of skilled labor and the need for increased efficiency drive the development of automated pruning solutions.
  • Robotic pruning offers a promising alternative to traditional manual methods.

Purpose of the Study:

  • To develop a method for automatic pruning point identification in jujube trees based on skeleton information.
  • To enable efficient and accurate robotic pruning of jujube orchards.
  • To provide a visual research basis for automated jujube tree management.

Main Methods:

  • Collected multi-view jujube tree data using an RGB-D camera to create a complete point cloud model.
  • Applied the space colonization algorithm to the point cloud to generate a tree skeleton.
  • Constructed a directed graph to represent the iterative relationships between skeleton points.
  • Developed a skeleton analysis algorithm to classify branches and identify pruning points based on defined rules.

Main Results:

  • Achieved registration errors of individual jujube trees less than 0.91 cm (average 0.66 cm), creating a reliable database for skeleton extraction.
  • The space colonization algorithm generated a skeleton structure with high coincidence to the actual jujube trees.
  • Identified pruning points were accurately located on the primary branches of the jujube trees.

Conclusions:

  • The proposed method effectively identifies pruning points on jujube trees using skeleton information.
  • The validity of the identified pruning points was successfully verified, supporting robotic pruning applications.
  • This research provides a valuable reference for pruning point location and a visual basis for automatic jujube pruning.