| --- |
| 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.* |
|
|
