Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)
Abstract
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 additionally demonstrate a phenotyping pipeline on the dataset, illustrating how segmentation, meshing and skeletonisation enable the computation of phenotypic traits or provision of data insights. Benchmarking is provided for the extracted traits against a manually curated baseline. 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.
Cite
Text
James et al. "Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-91835-3_4Markdown
[James et al. "Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/james2024eccvw-lincoln/) doi:10.1007/978-3-031-91835-3_4BibTeX
@inproceedings{james2024eccvw-lincoln,
title = {{Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)}},
author = {James, Katherine Margaret Frances and Heiwolt, Karoline and Sargent, Daniel James and Cielniak, Grzegorz},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {46-63},
doi = {10.1007/978-3-031-91835-3_4},
url = {https://mlanthology.org/eccvw/2024/james2024eccvw-lincoln/}
}