Datasets:
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Alternaria | |
| '1': Angular Spot | |
| '2': Fresh | |
| '3': Holed | |
| '4': Mosaic Virus | |
| splits: | |
| - name: train | |
| num_bytes: 1007582708 | |
| num_examples: 1227 | |
| download_size: 990926240 | |
| dataset_size: 1007582708 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| task_categories: | |
| - image-classification | |
| size_categories: | |
| - 1K<n<10K | |
| # Luffa Disease Classification | |
| This dataset provides real field images of luffa plants with disease symptoms, collected in Bangladesh using a handheld smartphone during October 2023. Captured in natural agricultural environments, the images offer a practical resource for developing computer vision models focused on crop disease classification. The dataset contains 1,227 images across 5 classes: Alternaria, Angular Spot, Fresh, Holed, Mosaic Virus. | |
| Images per class: | |
| - Alternaria: 270 | |
| - Angular Spot: 238 | |
| - Fresh: 256 | |
| - Holed: 222 | |
| - Mosaic Virus: 241 | |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. | |
| ## Citation | |
| ```bibtex | |
| @article{sheikh2024luffafolio, | |
| title={LuffaFolio: A Multidimensional Image Dataset of Smooth Luffa}, | |
| author={Sheikh, Md Ripon and Islam, Md. Masudul and Himel, Galib Muhammad Shahriar}, | |
| journal={Data in Brief}, | |
| volume={53}, | |
| pages={110149}, | |
| year={2024}, | |
| publisher={Elsevier} | |
| } | |
| ``` | |
| *This dataset was reformatted from its original format to match HuggingFace standards.* | |