Datasets:
File size: 1,711 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: split
dtype: string
- name: date
dtype: string
- name: location_code
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 420776401
num_examples: 48
download_size: 420791341
dataset_size: 420776401
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Chicory Root Segmentation
This dataset provides real-world RGB images of chicory roots in agricultural settings, captured for semantic segmentation tasks. The images depict roots in their natural growing environment, offering a realistic representation for developing and evaluating segmentation models in crop monitoring applications. The dataset contains 48 images with pixel-level mask annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the `split` column.
## Citation
```bibtex
@article{smith2020segmentation,
title={Segmentation of roots in soil with U-Net},
author={Smith, Abraham George and Petersen, Jens and Selvan, Raghavendra and Rasmussen, Camilla Ru{\o}},
journal={Plant Methods},
volume={16},
number={1},
pages={13},
year={2020},
publisher={Springer}
}
```
Smith, A. G., Petersen, J., Selvan, R., & Rasmussen, C. R. (2019). Data for paper 'Segmentation of Roots in Soil with U-Net' [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.3527713
*This dataset was reformatted from its original format to match HuggingFace standards.*
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