README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Anthracnose
          '1': algal leaf
          '2': bird eye spot
          '3': brown blight
          '4': gray light
          '5': healthy
          '6': red leaf spot
          '7': white spot
  splits:
  - name: train
    num_bytes: 780911770
    num_examples: 885
  download_size: 780957799
  dataset_size: 780911770
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
---
# Tea Sickness Classification

This dataset contains real field images of tea leaves affected by various diseases, collected in tea gardens across Anhui Province, China. Images were captured using a handheld iPhone 14 Pro Max with RGB imaging during October 2023, providing practical examples for agricultural disease detection research. The dataset contains 885 images across 8 classes: Anthracnose, algal leaf, bird eye spot, brown blight, gray light, healthy, red leaf spot, white spot.  
Images per class:
- Anthracnose: 100
- algal leaf: 113
- bird eye spot: 100
- brown blight: 113
- gray light: 100
- healthy: 74
- red leaf spot: 143
- white spot: 142

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

## Citation

```bibtex
@article{yang2025lightweight,
  title={Lightweight wavelet-CNN tea leaf disease detection},
  author={Yang, Jing and Xu, GaoJian and Yang, MengDao and Lin, ZhengPei},
  journal={PLOS One},
  volume={20},
  pages={e0323322},
  year={2025},
  publisher={Public Library of Science}
}
```


The dataset itself can be cited as:

Gibson Kimutai. (2022). *tea sickness dataset* [Dataset]. Mendeley. https://doi.org/10.17632/J32XDT2FF5.2


*This dataset was reformatted from its original format to match HuggingFace standards.*