Update README.md

README.md

@@ -29,4 +29,43 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 1K<n<10K
 ---
+
+# Sugarcane Leaf Disease Classification
+
+A dataset for image classification of Sugarcane Leaf Disease Classification. The dataset contains 6,748 images across 11 classes: Banded Chlorosis, Brown Spot, BrownRust, Dried, Grassy shoot, Healthy, Pokkah Boeng, Sett Rot, Smut, Viral Disease, Yellow Leaf.  
+Images per class:
+- Banded Chlorosis: 471
+- Brown Spot: 1,722
+- BrownRust: 314
+- Dried: 343
+- Grassy shoot: 346
+- Healthy: 430
+- Pokkah Boeng: 297
+- Sett Rot: 652
+- Smut: 316
+- Viral Disease: 663
+- Yellow Leaf: 1,194
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{thite2024sugarcane,
+  title={Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications},
+  author={Thite, Sandip and Suryawanshi, Yogesh and Patil, Kailas and Chumchu, Prawit},
+  journal={Data in brief},
+  volume={53},
+  pages={110268},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+Thite, Sandip; Suryawanshi, Yogesh; PATIL, Kailas; chumchu, prawit (2023), “Sugarcane Leaf Image Dataset”, Mendeley Data, V1, doi: 10.17632/9twjtv92vk.1