---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: year
    dtype: string
  - name: team
    dtype: string
  - name: crop
    dtype: string
  splits:
  - name: train
    num_bytes: 3846533840
    num_examples: 985
  download_size: 3846671041
  dataset_size: 3846533840
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Weed Segmentation France

This dataset comprises real RGB images of weeds in agricultural fields at Montoldre, France, specifically within maize and bean crop areas. Captured using a robot platform during 2019 and 2021, the images were collected under natural field conditions to support semantic segmentation research for weed detection in crop environments. The dataset contains 985 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{bertoglio2023comparative,
  title={A comparative study of Fourier transform and CycleGAN as domain adaptation techniques for weed segmentation},
  author={Bertoglio, Riccardo and Mazzucchelli, Alessio and Catalano, Nico and Matteucci, Matteo},
  journal={Smart Agricultural Technology},
  volume={4},
  pages={100188},
  year={2023},
  publisher={Elsevier}
}
```

Bertoglio, Riccardo; Mazzucchelli, Alessio; Catalano, Nico; Matteucci, Matteo (2023), “A Comparative Study of Fourier Transform and CycleGAN as Domain Adaptation Techniques for Weed Segmentation - Code and Data”, Politecnico di Milano, V2, doi: 10.17632/x8brgg2j28.2

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