| --- |
| 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.* |
|
|
