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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.*
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