Automated phenotyping of plants for breeding and plant studies promises to
provide quantitative metrics on plant traits at a previously unattainable
observation frequency. Developers of tools for performing high-throughput
phenotyping are, however, constrained by the availability of relevant datasets
on which to perform validation. To this end, we present a spatio-temporal
dataset of 3D point clouds of strawberry plants for two varieties, totalling 84
individual point clouds. We focus on the end use of such tools - the extraction
of biologically relevant phenotypes - and demonstrate a phenotyping pipeline on
the dataset. This comprises of the steps, including; segmentation,
skeletonisation and tracking, and we detail how each stage facilitates the
extraction of different phenotypes or provision of data insights. We
particularly note that assessment is focused on the validation of phenotypes,
extracted from the representations acquired at each step of the pipeline,
rather than singularly focusing on assessing the representation itself.
Therefore, where possible, we provide \textit{in silico} ground truth baselines
for the phenotypes extracted at each step and introduce methodology for the
quantitative assessment of skeletonisation and the length trait extracted
thereof. This dataset contributes to the corpus of freely available
agricultural/horticultural spatio-temporal data for the development of
next-generation phenotyping tools, increasing the number of plant varieties
available for research in this field and providing a basis for genuine
comparison of new phenotyping methodology.

自动表型分类技术可提供植物特征的定量度量，但开发者所能使用的验证数据集受限。本文提出了一个 3D 点云数据集，包含两个种类的草莓植物，共计 84 个点云。我们关注对这些工具的最终使用，即提取生物学相关的表型，并在数据集上演示了表型分类流程，其中包括分割、骨架化和跟踪等步骤，详细说明每个阶段如何促进不同表型的提取或数据的洞察。我们特别强调在验证表型时，重点放在从流程的每个阶段获取的表示中提取的表型，而不仅仅是评估表示本身。因此，我们在可能的情况下提供了每个步骤提取的表型的	extit {in silico} 基准，并介绍了骨架化及其提取的长度特征的定量评估方法。该数据集为农业 / 园艺领域的下一代表型分类工具的开发提供了免费的空间 - 时间数据，增加了可用于该领域研究的植物品种数量，并为新的表型分类方法提供了真实比较的基础。