Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach us- ing annotated real-world grapevine point clouds, demonstrating an improvement of 15.8% in the F1 score compared to the origi- nal Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices.
@inproceedings{dobbs2024accurate,
title={Accurate {3D} Grapevine Structure Extraction from High-Resolution Point Clouds},
author={Dobbs, Harry and Peat, Casey and Batchelor, Oliver and Atlas, James and Green, Richard},
booktitle={2024 International Conference on Image and Vision Computing New Zealand (ICVNZ2024)},
year={2024}
}