Datasets:
File size: 1,936 Bytes
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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.*
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