Datasets:

Project-AgML
/

chicory_root_segmentation

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

@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.