| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Carpetweeds |
| '1': Crabgrass |
| '2': Eclipta |
| '3': Goosegrass |
| '4': Morningglory |
| '5': Nutsedge |
| '6': PalmerAmaranth |
| '7': Prickly Sida |
| '8': Purslane |
| '9': Ragweed |
| '10': Sicklepod |
| '11': SpottedSpurge |
| '12': SpurredAnoda |
| '13': Swinecress |
| '14': Waterhemp |
| - name: date |
| dtype: string |
| - name: device |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 7072218033 |
| num_examples: 5187 |
| download_size: 10352091364 |
| dataset_size: 7072218033 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 1K<n<10K |
| --- |
| # Cottonweedid15 Classification |
|
|
| This dataset contains real-world RGB images of 15 common weed species found in cotton agricultural fields, captured under natural field conditions. The images depict weeds growing in typical cotton crop environments, providing a practical resource for developing and evaluating computer vision models for weed identification in precision agriculture applications. The dataset contains 5,187 images across 15 classes: Carpetweeds, Crabgrass, Eclipta, Goosegrass, Morningglory, Nutsedge, PalmerAmaranth, Prickly Sida, Purslane, Ragweed, Sicklepod, SpottedSpurge, SpurredAnoda, Swinecress, Waterhemp. |
| Images per class: |
| - Carpetweeds: 763 |
| - Crabgrass: 111 |
| - Eclipta: 254 |
| - Goosegrass: 216 |
| - Morningglory: 1,115 |
| - Nutsedge: 273 |
| - PalmerAmaranth: 689 |
| - Prickly Sida: 129 |
| - Purslane: 450 |
| - Ragweed: 129 |
| - Sicklepod: 240 |
| - SpottedSpurge: 234 |
| - SpurredAnoda: 61 |
| - Swinecress: 72 |
| - Waterhemp: 451 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{chen2022performance, |
| title={Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems}, |
| author={Chen, Dong and Lu, Yuzhen and Li, Zhaojian and Young, Sierra}, |
| journal={Computers and Electronics in Agriculture}, |
| volume={198}, |
| pages={107091}, |
| year={2022}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| https://www.kaggle.com/datasets/yuzhenlu/cottonweedid15 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
