---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Citrus_leafminer
          '1': Fe
          '2': Greasy_spot
          '3': HLB
          '4': Healthy
          '5': Mg
          '6': Mn
          '7': 'N'
          '8': Red_scale
          '9': Red_scale_sequelae
          '10': Texas_mite
          '11': Zn
  splits:
  - name: train
    num_bytes: 1884058990
    num_examples: 953
  download_size: 1884130526
  dataset_size: 1884058990
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- image-classification
license: cc-by-4.0
size_categories:
- n<1K
---

# CitrusUAT Disease Classification

A dataset for disease classification of orange leaves. The dataset contains 953 images across 12 classes: Citrus_leafminer, Fe, Greasy_spot, HLB, Healthy, Mg, Mn, N, Red_scale, Red_scale_sequelae, Texas_mite, Zn.  
Images per class:
- Citrus_leafminer: 100
- Fe: 100
- Greasy_spot: 100
- HLB: 43
- Healthy: 100
- Mg: 100
- Mn: 30
- N: 50
- Red_scale: 30
- Red_scale_sequelae: 100
- Texas_mite: 100
- Zn: 100

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{gomez2024citrusuat,
  title={CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques},
  author={G{\'o}mez-Flores, Wilfrido and Garza-Salda{\~n}a, Juan Jos{\'e} and Varela-Fuentes, S{\'o}stenes Edmundo},
  journal={Data in brief},
  volume={52},
  pages={109908},
  year={2024},
  publisher={Elsevier}
}
```

Wilfrido Gómez Flores. (2023). CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques [Data set]. In CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques (1.0, Vol. 52, p. 109908). Zenodo. https://doi.org/10.5281/zenodo.8294078