# Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)

arXiv: https://arxiv.org/abs/2403.00566 · HTML: https://mlsanity.com/paper/2403.00566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [SAM3D: Segment Anything in 3D Scenes](https://mlsanity.com/paper/2306.03908/) (2023)
- [Statistical shape representations for temporal registration of plant components in 3D](https://mlsanity.com/paper/2209.11526/) (2022)
- [Robust Skeletonization for Plant Root Structure Reconstruction from MRI](https://mlsanity.com/paper/2010.14440/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics](https://mlsanity.com/paper/2407.13304/) (2024)

## Abstract (arXiv)

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 textitin 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.