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Related Experiment Video

Updated: Jan 16, 2026

Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Towards Robotic Pruning: Automated Annotation and Prediction of Branches for Pruning on Trees Reconstructed Using

Jana Dukić¹, Petra Pejić¹, Ivan Vidović¹

¹Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osijek, Croatia.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This study introduces an automated pipeline for predicting fruit tree branches needing pruning. It uses 3D reconstruction and machine learning for precise robotic pruning in orchards.

Keywords:

3D tree reconstruction RGB-D images automatic annotation autonomous fruit tree pruning

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Published on: March 5, 2015

Area of Science:

Agricultural Engineering
Computer Vision
Robotics

Background:

Precision agriculture demands efficient and automated pruning systems.
Current robotic pruning lacks accurate, real-time branch identification.

Purpose of the Study:

To develop a comprehensive pipeline for automated pruning prediction in fruit trees.
To enable precise and efficient robotic pruning through advanced 3D modeling and machine learning.

Main Methods:

Multi-view RGB-D image capture for 3D reconstruction of pear trees using TEASER++.
Automatic branch labeling by comparing pre- and post-pruning 3D models.
Training a PointNet++ neural network for direct pruning prediction on point clouds.

Main Results:

Successful 3D reconstruction and automated labeling of pruneable branches.
A trained neural network capable of real-time pruning prediction directly on point clouds.
Promising performance metrics and visual validation of the automated pipeline.

Conclusions:

The developed pipeline offers a robust foundation for autonomous orchard management.
This approach significantly enhances the precision, speed, and practicality of robotic pruning systems.
Future work will focus on refining accuracy and expanding applicability to diverse orchard environments.