---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 642849737
    num_examples: 148
  download_size: 642872342
  dataset_size: 642849737
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Grapevine Organ Segmentation

This dataset provides real-world RGB images of grapevine organs captured in field environments across two locations in Italy (Piacenza and Alseno) between 2018 and 2021. Images were acquired using standard RGB cameras under natural agricultural conditions, focusing on plant structures such as leaves and stems. It serves as a field-based resource for semantic segmentation tasks in viticulture and crop monitoring applications. The dataset contains 148 images with pixel-level mask annotations.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{guadagna2023deep,
  title={Using deep learning for pruning region detection and plant organ segmentation in dormant spur-pruned grapevines},
  author={Guadagna, P. and Fernandes, M. and Chen, F. and Santamaria, A. and Teng, T. and Frioni, T. and Caldwell, D. G. and Poni, S. and Semini, C. and Gatti, M.},
  journal={Precision Agriculture},
  volume={24},
  pages={1547-1569},
  year={2023},
  publisher={Springer US}
}
```

Miguel Fernandes, Paolo Guadagna, & Alessandro Santamaria. (2021). Grapevine Dataset for plant organ segmentation (Version v1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.5501784

*This dataset was reformatted from its original format to match HuggingFace standards.*