  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.
