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Lincoln's Annotated Spatio-Temporal Strawberry Dataset

2024-03-01 14:44:05
Katherine Margaret Frances James, Karoline Heiwolt, Daniel James Sargent, Grzegorz Cielniak


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.

Abstract (translated)

自动植物表型鉴定为育种和植物研究提供了植物特征的定量指标,这是以前无法达到的观察频率。然而,开发高吞吐量表型检测工具的开发者却受到相关数据集可用性的限制。为此,我们报道了一个草莓植物三个维点云的 spatio-temporal 3D 点数据集,共包括 84 个个体点云。我们重点关注这些工具的最终用途——提取生物相关表型——并在数据集中展示了表型鉴定过程。这包括分割、骨架化和跟踪步骤,并且我们详细说明每个阶段如何促进提取不同表型或提供数据洞察。我们特别指出评估的重点在于验证从管道中提取到的表型的验证,而不是单纯地关注评估表型本身。因此,在可能的情况下,我们为每个步骤提取的表型提供了 \textit{in silico} 真实值基线,并引入了评估骨架化和长度表型的量化方法的 methodology。这个数据集为开发下一代表型检测工具贡献了免费的农业/园艺时空数据,增加了研究领域的植物品种数量,并为真正比较新的表型鉴定方法提供了基础。



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