Datasets:
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Alternaria | |
| '1': Bacterial Blight | |
| '2': Cercospora | |
| '3': Grey Mildew | |
| '4': Healthy | |
| splits: | |
| - name: train | |
| num_bytes: 1294179547 | |
| num_examples: 132 | |
| download_size: 1294198933 | |
| dataset_size: 1294179547 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| task_categories: | |
| - image-classification | |
| size_categories: | |
| - n<1K | |
| # Cotton Disease Classification | |
| This dataset comprises RGB images of cotton plants collected in field environments, featuring a mix of real agricultural observations and synthetic representations of disease conditions. It captures visual variations of cotton foliage under typical farming settings, providing a resource for developing computer vision models in agricultural disease detection. The dataset contains 132 images across 5 classes: Alternaria, Bacterial Blight, Cercospora, Grey Mildew, Healthy. | |
| Images per class: | |
| - Alternaria: 25 | |
| - Bacterial Blight: 23 | |
| - Cercospora: 40 | |
| - Grey Mildew: 25 | |
| - Healthy: 19 | |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. | |
| ## Citation | |
| ```bibtex | |
| @article{aslam2025multi, | |
| title={Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm}, | |
| author={Aslam, Afira and Usman, Syed Muhammad and Zubair, Muhammad and Yasin, Amanullah and Owais, Muhammad and Hussain, Irfan}, | |
| journal={PLOS One}, | |
| volume={20}, | |
| pages={e0324293}, | |
| year={2025}, | |
| publisher={Public Library of Science} | |
| } | |
| ``` | |
| The dataset itself can be cited as: | |
| Bhagya Patil. (2021). *Cotton Leaf Dataset* [Dataset]. Mendeley. https://doi.org/10.17632/6HM6PC3Y43.2 | |
| *This dataset was reformatted from its original format to match HuggingFace standards.* | |