| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Bipolaris_Maydis |
| '1': Common_Rust |
| '2': Curvularia_Lunata(wakker)boed_Spot |
| '3': Northern-Leaf-Blight |
| '4': Own-Spot |
| '5': Sheath-Blight |
| splits: |
| - name: train |
| num_bytes: 69223597 |
| num_examples: 600 |
| download_size: 69234645 |
| dataset_size: 69223597 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - n<1K |
| --- |
| # Corn Leaf Disease Classification |
|
|
| This dataset provides real RGB images of corn leaves affected by various diseases, captured in field environments across northern Anhui Province, China. Images were acquired using a NIKON D810 camera at 1001x1500 pixels resolution, delivering high-fidelity visual data for agricultural disease detection research. The dataset contains 600 images across 6 classes: Bipolaris_Maydis, Common_Rust, Curvularia_Lunata(wakker)boed_Spot, Northern-Leaf-Blight, Own-Spot, Sheath-Blight. |
| Images per class: |
| - Bipolaris_Maydis: 100 |
| - Common_Rust: 100 |
| - Curvularia_Lunata(wakker)boed_Spot: 100 |
| - Northern-Leaf-Blight: 100 |
| - Own-Spot: 100 |
| - Sheath-Blight: 100 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{bai2025lightweight, |
| title={A lightweight model and corn leaf disease recognition}, |
| author={Bai, Lujie and Zhu, Shaoqiu and Gao, Haitao}, |
| journal={PLOS One}, |
| volume={20}, |
| pages={e0336945}, |
| year={2025}, |
| publisher={Public Library of Science} |
| } |
| ``` |
|
|
|
|
| The dataset itself can be cited as: |
|
|
| Bai, L. (2025). *A lightweight model and corn leaf disease recognition* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.30264037.V6 |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
