| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Healthy |
| '1': MLN |
| '2': MSV |
| splits: |
| - name: train |
| num_bytes: 7911968327 |
| num_examples: 9356 |
| download_size: 7528242288 |
| dataset_size: 7911968327 |
| 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 Disease Classification |
|
|
| A dataset for disease classification of Maize leaves. The dataset contains 9,356 images across 3 classes: Healthy, MLN, MSV. |
| Images per class: |
| - Healthy: 3,073 |
| - MLN: 3,231 |
| - MSV: 3,052 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{mduma2024updating, |
| title={Updating “machine learning imagery dataset for maize crop: A case of Tanzania” with expanded data to cover the new farming season}, |
| author={Mduma, Neema and Mayo, Flavia}, |
| journal={Data in Brief}, |
| volume={54}, |
| pages={110359}, |
| year={2024}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Mduma, Neema; Mayo, Flavia (2023), “Maize Imagery Dataset - Tanzania”, Mendeley Data, V1, doi: 10.17632/fkw49mz3xs.1 |
