Towards Robotic Pruning: Automated Annotation and Prediction of Branches for Pruning on Trees Reconstructed Using RGB-D Images
Year: 2025
Type: article
Abstract: This paper presents a comprehensive pipeline for automated prediction of branches to be pruned, integrating 3D reconstruction of fruit trees, automatic branch labeling, and pruning prediction. The workflow begins with capturing multi-view RGB-D images in orchard settings, followed by generating and preprocessing point clouds to reconstruct partial 3D models of pear trees using the TEASER++ algorithm. Differences between pre- and post-pruning models are used to automatically label branches to be pruned, creating a valuable dataset for both reconstruction methods and training machine learning models. A neural network based on PointNet++ is trained to predict branches to be pruned directly on point clouds, with performance evaluated through quantitative metrics and visual inspections. The pipeline demonstrates promising results, enabling real-time prediction suitable for robotic implementation. While some inaccuracies remain, this work lays a solid foundation for future advancements in autonomous orchard management, aiming to improve precision, speed, and practicality of robotic pruning systems.
Source: Sensors
DOI: 10.3390/s25185648
Institution: University of Osijek
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4414091641