Create README.md

README.md

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+---
+configs:
+- config_name: raw
+  default: true
+  data_dir: raw
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': cercospora
+          '1': healthy
+          '2': mites_and_trips
+          '3': nutritional
+          '4': powdery mildew
+- config_name: augmented
+  data_dir: augmented
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': cercospora
+          '1': healthy
+          '2': mites_and_trips
+          '3': nutritional
+          '4': powdery mildew
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 10K<n<100K
+---
+
+# COLD Chili Leaf Disease Classification
+
+A dataset for disease classification of chili leaves. The dataset contains raw and augmented versions.  
+The raw dataset contains 532 images.  
+Images per class:
+- cercospora: 152
+- healthy: 69
+- mites_and_trips: 107
+- nutritional: 102
+- powdery mildew: 102
+
+The augmented dataset contains 10,974 images.  
+Images per class:
+- cercospora: 2,217
+- healthy: 2,195
+- mites_and_trips: 2,504
+- nutritional: 2,029
+- powdery mildew: 2,029
+
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{aishwarya2024dataset,
+  title={Dataset of chilli and onion plant leaf images for classification and detection},
+  author={Aishwarya, MP and Reddy, A Padmanabha},
+  journal={Data in Brief},
+  volume={54},
+  pages={110524},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+M P, Aishwarya; Reddy, Padmanabha  (2024), “chilli dataset”, Mendeley Data, V2, doi: 10.17632/tf9dtfz9m6.2