| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: split |
| dtype: string |
| - name: '0.0' |
| dtype: float64 |
| - name: project |
| dtype: string |
| - name: location |
| dtype: string |
| - name: cultivar |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 47271221 |
| num_examples: 528 |
| download_size: 47283105 |
| dataset_size: 47271221 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - other |
| size_categories: |
| - n<1K |
| --- |
| # Grapevine Roots Phenotyping |
|
|
| This dataset provides ground truth RGB images of grapevine roots captured in a field environment at the Ramat Negev Research and Development Center in Israel. The images were collected using handheld minirhizotron cameras and an I-CAP system during 2012-2013. The dataset contains 528 images, each paired with the following ground-truth measurement(s): 0.0. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| The original train/test/val split has been preserved in the `split` column. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{khoroshevsky2024cnn, |
| title={A CNN-based framework for estimation of root length, diameter, and color from in situ minirhizotron images}, |
| author={Khoroshevsky, Faina and Zhou, Kaining and Bar-Hillel, Aharon and Hadar, Ofer and Rachmilevitch, Shimon and Ephrath, Jhonathan E. and Lazarovitch, Naftali and Edan, Yael}, |
| journal={Computers and Electronics in Agriculture}, |
| volume={227}, |
| pages={109457}, |
| year={2024}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Faina Khoroshevsky, Kaining Zhou, & Naftali Lazarovitch. (2024). Dataset of Grapevine roots with length, diameter, and color annotations [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10727134 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
