| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': atriplex |
| '1': chenopodium |
| '2': convolvulus |
| '3': cyperus |
| '4': datura |
| '5': lolium |
| '6': maize |
| '7': portulaca |
| '8': salsola |
| '9': solanum |
| '10': sorghum |
| '11': tomato |
| - name: crop |
| dtype: string |
| - name: growth_stage |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 95541077 |
| num_examples: 55828 |
| download_size: 642109490 |
| dataset_size: 95541077 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 10K<n<100K |
| --- |
| # Maize Tomato Weed Classification |
|
|
| A dataset for classification of weeds and crops in maize and tomato fields. The dataset contains 55,828 images across 12 classes: |
| Images per class: |
| - atriplex: 2,459 |
| - chenopodium: 3,375 |
| - convolvulus: 2,302 |
| - cyperus: 3,215 |
| - datura: 1,271 |
| - lolium: 1,063 |
| - maize: 25,273 |
| - portulaca: 2,052 |
| - salsola: 2,409 |
| - solanum: 4,084 |
| - sorghum: 1,703 |
| - tomato: 6,622 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{mesias2025drone, |
| title={Drone imagery dataset for early-season weed classification in maize and tomato crops}, |
| author={Mes{\'\i}as-Ruiz, Gustavo A and Pe{\~n}a, Jos{\'e} M and de Castro, Ana I and Dorado, Jos{\'e}}, |
| journal={Data in Brief}, |
| volume={58}, |
| pages={111203}, |
| year={2025}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Mesías-Ruiz, G. A., Peña Barragán, J. M., Castro, A. I. D., & Dorado, J. (2024). DIWEED: Drone Imagery dataset for early-season WEED classification [Data set]. DIGITAL.CSIC. http://doi.org/10.20350/DIGITALCSIC/16559 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
