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README.md

@@ -26,4 +26,45 @@ configs:
   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.*