| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: crop |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float64 |
| - name: categories |
| list: |
| class_label: |
| names: |
| '0': Blackbean |
| '1': Canola |
| '2': Corn |
| '3': Field Pea |
| '4': Flax |
| '5': Horseweed |
| '6': Kochia |
| '7': Lentil |
| '8': Ragweed |
| '9': Redroot Pigweed |
| '10': Soybean |
| '11': Sugar beet |
| '12': Waterhemp |
| splits: |
| - name: train |
| num_bytes: 7867132445 |
| num_examples: 1120 |
| download_size: 7802827920 |
| dataset_size: 7867132445 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Weed Crop Detection |
|
|
| A dataset for object detection of weeds and crops in fields. The dataset contains 1,120 images with 17,693 bounding box annotations across 13 categories. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{upadhyay2025weed, |
| title={Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems}, |
| author={Upadhyay, Arjun and Mahecha, Maria Villamil and Mettler, Joseph and Howatt, Kirk and Aderholdt, William and Ostlie, Michael and Sun, Xin and others}, |
| journal={Data in Brief}, |
| volume={60}, |
| pages={111486}, |
| year={2025}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Upadhyay, Arjun; G C, Sunil; Villamil Mahecha, Maria; Mettler, Joseph; Howatt, Kirk; Aderholdt, William; Ostlie, Michael; Sun, Xin (2025), “Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems”, Mendeley Data, V2, doi: 10.17632/mthv4ppwyw.2--> |
