---
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.*