# 3D Skeletonization of Complex Grapevines for Robotic Pruning

arXiv: https://arxiv.org/abs/2307.11706 · HTML: https://mlsanity.com/paper/2307.11706/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Bumblebee: A Path Towards Fully Autonomous Robotic Vine Pruning](https://mlsanity.com/paper/2112.00291/) (2021)
- [Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation](https://mlsanity.com/paper/2106.04208/) (2021)

## Built on by (most cited first, 3 of 3)

- [Synthetic Data Generation for Anomaly Detection on Table Grapes](https://mlsanity.com/paper/2412.12949/) (2024)
- [FloPE: Flower Pose Estimation for Precision Pollination](https://mlsanity.com/paper/2503.11692/) (2025)
- [Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics](https://mlsanity.com/paper/2504.00032/) (2025)

## Abstract (arXiv)

Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.