| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float64 |
| - name: categories |
| list: |
| class_label: |
| names: |
| '0': ragweed |
| '1': waterhemp |
| '2': horseweed |
| '3': redrootpigweed |
| '4': kochia |
| splits: |
| - name: train |
| num_bytes: 4554904299 |
| num_examples: 551 |
| download_size: 4554986069 |
| dataset_size: 4554904299 |
| 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 |
| --- |
| # ImageWeeds Aerial Weed Detection |
|
|
| A dataset for object detection of weeds within crop fields. The dataset contains 551 images with 2,388 bounding box annotations across 5 categories. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{rai2023multi, |
| title={Multi-format open-source weed image dataset for real-time weed identification in precision agriculture}, |
| author={Rai, Nitin and Mahecha, Maria Villamil and Christensen, Annika and Quanbeck, Jamison and Zhang, Yu and Howatt, Kirk and Ostlie, Michael and Sun, Xin}, |
| journal={Data in Brief}, |
| volume={51}, |
| pages={109691}, |
| year={2023}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Rai, Nitin; Villamil Mahecha, Maria; Christensen, Annika; Quanbeck, Jamison; Howatt, Kirk; Ostlie, Michael; Zhang, Yu; Sun, Xin (2023), “ImageWeeds: An Image dataset consisting of weeds in multiple formats to advance computer vision algorithms for real-time weed identification and spot spraying application”, Mendeley Data, V2, doi: 10.17632/8kjcztbjz2.2 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
