cff-version: 1.2.0
abstract: "<p>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-from silhouette methodology (Golbach et al., 2016). The resulting dataset was annotated and separated into three parts. 1) Annotated point clouds and corresponding RGB images with semantic and instance labels , 2) annotated skeletons to analyse plant architecture, and 3) manual reference measurements of internode length, internode diameter, leaf angle,  and phyllotactic angle to evaluate phenotyping algorithms from point cloud to plant traits.</p>"
authors:
  - family-names: Marrewijk, van
    given-names: Bart M.
    orcid: "https://orcid.org/0000-0003-3681-8114"
  - family-names: van Daalen
    given-names: Tim
    orcid: "https://orcid.org/0009-0003-0308-1969"
  - family-names: Smoleňová
    given-names: Katarína
  - family-names: Xin
    given-names: Bolai
  - family-names: Polder
    given-names: Gerrit
    orcid: "https://orcid.org/0000-0003-4896-4776"
  - family-names: Kootstra
    given-names: Gert
    orcid: "https://orcid.org/0000-0002-2579-4324"
title: "TomatoWUR: an annotated dataset of 3D tomato plants to quantitatively evaluate segmentation, skeletonisation, and plant trait extraction algorithms for 3D plant phenotyping"
keywords:
version: 2
identifiers:
  - type: doi
    value: 10.4121/e2c59841-4653-45de-a75e-4994b2766a2f.v2
license: CC BY-SA 4.0
date-released: 2025-04-30