The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/e2c59841-4653-45de-a75e-4994b2766a2f

Marrewijk, van, Bart M.; Xin, Bolai; van Daalen, Tim; van Henten, Eldert; Polder, Gerrit et. al. (2025): TomatoWUR: an annotated dataset of 3D tomato plants to quantitatively evaluate segmentation, skeletonisation, and plant trait extraction algorithms for 3D plant phenotyping. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/e2c59841-4653-45de-a75e-4994b2766a2f.v1

Categories

• Artificial Intelligence and Image Processing
• Horticultural Production
• Agriculture, Land and Farm Management
• Agricultural and Veterinary Sciences
• Information and Computing Sciences

Keywords

3D phenotyping 3D plant phenotyping horticulture plant architecture point clouds RGB images segmentation shape-from-silhouette tomato

Geolocation

view on openstreetmap

Licence

CC BY-SA 4.0

Export as...

RefWorks BibTeX Reference Manager Endnote DataCite NLM DC CFF

by Bart M. Marrewijk, van , Bolai Xin, Tim van Daalen , Eldert van Henten , Gerrit Polder , Gert Kootstra

In this research, we created a dataset of 44 annotated 3D point clouds of tomato plants. Images were captured using fifteen cameras surrounding a single plant. Those images were used to create a point cloud using the shape-form silhouette methodology (Koenderink et al., 2009). The resulting dataset was annotated and separated into three parts. 1) Annotated point clouds with semantic and instance labels. The annotated point clouds were used to render annotated RGB images. 2) Annotated skeletons to analyse plant architecture. 3) Manual measurements of internode length, stem thickness, branching and phyllotactic angle to evaluate phenotyping algorithms from point cloud to plant traits. To view the dataset, please have a look at our git:

History

• 2025-03-27 first online, published, posted

Publisher

4TU.ResearchData

Format

Raw images: PNG; Annotated RGB images: PNG; Camera calibration: json; Point clouds: csv, including coordinates (xyz), colour (r,g,b), normals (nx, ny, nz), Annotated point clouds: csv; Annotated skeletons including manual measurements: csv

References

• https://github.com/WUR-ABE/TomatoWUR/tree/main

Funding

• Test and Experiment Facilities for the Agri-Food Domain (grant code 101100622) [more info...] Digital Europe Programme (DIGITAL)

Organizations

Greenhouse Horticulture Business Unit, Wageningen University & Research