---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: date
    dtype: timestamp[s]
  - name: band
    dtype: string
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 343024917
    num_examples: 780
  download_size: 343074669
  dataset_size: 343024917
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Weedsgalore Maize Segmentation

This dataset provides real-world RGB imagery captured in maize agricultural fields, focusing on weed presence within crop canopies. It includes pixel-level segmentation masks to delineate weeds from maize plants, supporting computer vision research in precision agriculture applications. The dataset contains 780 images with pixel-level mask annotations.

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
@inproceedings{celikkan2025weedsgalore,
  title={WeedsGalore: A multispectral and multitemporal UAV-based dataset for crop and weed segmentation in agricultural maize fields},
  author={Celikkan, Ekin and Kunzmann, Timo and Yeskaliyev, Yertay and Itzerott, Sibylle and Klein, Nadja and Herold, Martin},
  booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  pages={4767--4777},
  year={2025},
  organization={IEEE}
}
```

https://github.com/GFZ/weedsgalore

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