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

Abstract

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.

Plant phenotyping relevance

キュウリの3D点群から節間長を推定する画像解析手法を開発し、既存の2D手法と精度比較しているため、植物表現型取得法が研究の中心である。

Code and data availability

The supplied blocks describe the cucumber 2D image (9990 images) and 3D point cloud (264 scans) dataset, manual annotations, and PointNet++-based method, but contain no data or code availability statement, no public repository, and no authors' URL for the dataset, annotations, trained model, or analysis code. All URLs/

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