---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 147434568
    num_examples: 20
  download_size: 147439317
  dataset_size: 147434568
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nc-sa-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Onion Weed Segmentation

This dataset provides real RGB imagery of onion fields with weed infestations, captured directly in agricultural field environments. It is designed for semantic segmentation tasks to identify and delineate weed areas within onion crop contexts under typical farming conditions. The dataset contains 20 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{bosilj2020transfer,
  title={Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},
  author={Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},
  journal={Journal of Field Robotics},
  volume={37},
  number={1},
  pages={7--19},
  year={2020},
  publisher={Wiley Online Library}
}
```

Petra Bosilj, Erchan Aptoula, Tom Duckett, and Grzegorz Cielniak: “Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture”, Journal of Field Robotics (2019)

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