The added value of 3D point clouds for digital plant phenotyping – A case study on internode length measurements in cucumber

Frans P. Boogaard*, Eldert J. van Henten, Gert Kootstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


Computer-vision based methods contribute to the availability of high-quality phenotypic datasets. Most computer-vision based methods for plant phenotyping are based on analysis of 2D images. However, previous research showed that for traits related to plant architecture, like internode length, a main limitation of 2D methods was that plants with a curved growing pattern could not be accurately measured. In this work, it was hypothesised that methods based on 3D data can overcome this limitation, while increasing the overall accuracy of the internode length measurements. To test the hypothesis, a method was proposed to estimate internode lengths from 3D point clouds of cucumber plants. First, a deep neural network based on PointNet++ was trained to segment the point clouds into plant parts. The points that were predicted as ‘node’ were then selected and a clustering algorithm was used to group points belonging to the same node. The Euclidean distance between the detected nodes was used as an estimate of the internode length. The results were compared to the results of a previously published method based on 2D images. The results of the 3D method were significantly more accurate than the results of the 2D method. Moreover, in contrast to the 2D method, the internode length estimates of the 3D method were equally accurate for curved plants as well as for straight plants. The results clearly demonstrated that computer-vision based methods to measure plant architecture in general, and more specifically to measure internode length, greatly benefit from the availability of 3D data.

Original languageEnglish
Number of pages12
JournalBiosystems Engineering
Publication statusPublished - Oct 2023


  • 3D point cloud
  • Deep learning
  • Digital plant phenotyping
  • Internode length
  • Plant architecture
  • Plant-part segmentation


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