Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation
Not on the map. Why?
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.
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
8 of 14 references.
References · 8
- 2017Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNscited by 55
- 2019Grape detection, segmentation and tracking using deep neural networks and three-dimensional associationcited by 21
- 2016Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchardscited by 19
- 2016Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detectioncited by 14
- 2018Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Networkcited by 7
- 2018Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Imagescited by 4
- 2018An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networkscited by 3
- 2019Deep Mangoes: from fruit detection to cultivar identification in colour images of mango treescited by 3
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.