---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: variant
    dtype: string
  splits:
  - name: train
    num_bytes: 326073180
    num_examples: 70
  download_size: 326080167
  dataset_size: 326073180
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
size_categories:
- n<1K
---
# Deepcanola Br11

This dataset contains unlabeled RGB images of canola (Brassica napus) plants in field environments, featuring a mix of real field captures and synthetic data. It provides a diverse resource for agricultural computer vision research focused on crop segmentation tasks within natural farming settings. The dataset contains 70 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{vanvliet2025deepcanola,
  title={DeepCanola: Phenotyping brassica pods using semi-synthetic data and active learning},
  author={van Vliet, Larissa J.J. and Atkins, Kieran and Kurup, Smita and Siles, Laura and Hepworth, Jo and Corke, Fiona M.K. and Doonan, John H. and Lu, Chuan},
  journal={Computers and Electronics in Agriculture},
  volume={237},
  pages={110470},
  year={2025},
  publisher={Elsevier}
}
```

Atkins, K., van Vliet, L., Doonan, J., & Lu, C. (2024). DeepCanola: Phenotyping Brassica Pods Using Semi-Synthetic Data and Active Learning [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14235543

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