ArticleSensors (Basel, Switzerland)2018
High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Characterization of Bunch Compactness in a Diverse Collection ofPlants (Basel, Switzerland) · 2025Article
- Unsupervised monocular depth estimation with omnidirectional camera for 3D reconstruction of grape berries in the wild.PloS one · 2025Article
- A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce.Sensors (Basel, Switzerland) · 2023Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Deep incorporation of organic amendments into soils of a 'Calardis Musqué' vineyard: effects on greenhouse gas emissions, vine vigor, and grape quality.Frontiers in plant science · 2023Article
- DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot.Frontiers in plant science · 2023Article
- Introducing Three-Dimensional Scanning for Phenotyping of Olive Fruits Based on an Extensive Germplasm Survey.Plants (Basel, Switzerland) · 2022Article
- Analysis of Depth Cameras for Proximal Sensing of Grapes.Sensors (Basel, Switzerland) · 2022Article
- Article
- Towards Sensor-Based Phenotyping of Physical Barriers of Grapes to Improve Resilience toFrontiers in plant science · 2021Article
- Differential expression of transcription factor- and further growth-related genes correlates with contrasting cluster architecture in Vitis vinifera 'Pinot Noir' and Vitis spp. genotypes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Fluorescence-Sensor Mapping for the in Vineyard Non-Destructive Assessment of Crimson Seedless Table Grape Quality.Sensors (Basel, Switzerland) · 2020Article
- Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield.Horticulture research · 2020Article
- Characterizing 3D inflorescence architecture in grapevine using X-ray imaging and advanced morphometrics: implications for understanding cluster density.Journal of experimental botany · 2019Article
- Identification of co-located QTLs and genomic regions affecting grapevine cluster architecture.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model.Sensors (Basel, Switzerland) · 2019Article
- Crop Phenomics: Current Status and Perspectives.Frontiers in plant science · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phenotyping labor-intensive and time-consuming. In the present study, a fast and high-precision phenotyping pipeline was developed. The optical sensor Artec Spider 3D scanner (Artec 3D, L-1466, Luxembourg) was used to generate dense 3D point clouds of grapevine bunches under lab conditions and an automated analysis software called 3D-Bunch-Tool was developed to extract different single 3D bunch traits, i.e., the number of berries, berry diameter, single berry volume, total volume of berries, convex hull volume of grapes, bunch width and bunch length. The method was validated on whole bunches of different grapevine cultivars and phenotypic variable breeding material. Reliable phenotypic data were obtained which show high significant correlations (up to r² = 0.95 for berry number) compared to ground truth data. Moreover, it was shown that the Artec Spider can be used directly in the field where achieved data show comparable precision with regard to the lab application. This non-invasive and non-contact field application facilitates the first high-precision phenotyping pipeline based on 3D bunch traits in large plant sets.
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Registered trials
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