---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: leaf_position
    dtype: string
  splits:
  - name: train
    num_bytes: 1258931272
    num_examples: 973
  download_size: 1259067273
  dataset_size: 1258931272
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
size_categories:
- n<1K
---
# Spring Wheat Stomata Imprint

This dataset contains high-resolution RGB images of stomata imprints from spring wheat and faba bean plants, captured in a field environment at Taastrup campus, Denmark. The images were acquired using a fixed platform with a Leica DM750 light microscope and ICC50 HD digital microscope camera during the collection period from December 2021 to Fall 2022. The dataset contains 973 images with no classification, segmentation, or bounding-box annotations.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{wacker2025stomata,
  title={Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?},
  author={Wacker, Tomke S. and Smith, Abraham G. and Jensen, Signe M. and Pflüger, Theresa and Hertz, Viktor G. and Rosenqvist, Eva and Liu, Fulai and Dresbøll, Dorte B.},
  journal={Plant Methods},
  volume={21},
  pages={95},
  year={2025},
  publisher={BioMed Central}
}
```

Wacker, T. S., Smith, A. G., Jensen, S. M., Pflüger, T., Herz, V., Rosenqvist, E., Liu, F., & Dresbøll, D. B. (2025). Datasets used in "Stomata Morphology Measurement with interactive Machine Learning: Accuracy, Speed, and Biological Relevance?" [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15316123

*This dataset was reformatted from its original format to match HuggingFace standards.*