| --- |
| 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.* |
|
|
