---
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