# Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation

arXiv: https://arxiv.org/abs/2106.04208 · HTML: https://mlsanity.com/paper/2106.04208/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](https://mlsanity.com/paper/1907.11819/) (2019)
- [Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards](https://mlsanity.com/paper/1610.08120/) (2016)
- [Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection](https://mlsanity.com/paper/1612.03019/) (2016)
- [Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network](https://mlsanity.com/paper/1806.06198/) (2018)
- [Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images](https://mlsanity.com/paper/1805.11778/) (2018)
- [An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks](https://mlsanity.com/paper/1811.09561/) (2018)
- [Deep Mangoes: from fruit detection to cultivar identification in colour images of mango trees](https://mlsanity.com/paper/1909.10939/) (2019)

## Built on by (most cited first, 2 of 2)

- [3D Skeletonization of Complex Grapevines for Robotic Pruning](https://mlsanity.com/paper/2307.11706/) (2023)
- [Towards Precise Pruning Points Detection using Semantic-Instance-Aware Plant Models for Grapevine Winter Pruning Automation](https://mlsanity.com/paper/2109.07247/) (2021)

## Abstract (arXiv)

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points.