---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: length_mm
    dtype: float64
  - name: area_mm2
    dtype: float64
  splits:
  - name: train
    num_bytes: 4350284008
    num_examples: 891
  download_size: 4350390038
  dataset_size: 4350284008
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- other
size_categories:
- n<1K
---
# Deepcanola Br9

This dataset provides ground truth annotations for crop segmentation of canola (Brassica napus) in agricultural field environments. It features a mixed collection of real and synthetic RGB imagery captured under typical outdoor farming conditions. The resource supports computer vision research focused on phenotyping and segmentation tasks for canola crops in real-world agricultural settings. The dataset contains 891 images, each paired with the following ground-truth measurement(s): area_mm2, length_mm.

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.*