Update README.md

README.md

@@ -52,7 +52,59 @@ configs:
   - split: train
     path: augmented/train-*
 - config_name: raw
+  default: true
   data_files:
   - split: train
     path: raw/train-*
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 1K<n<10K
 ---
+
+# Banana Leaf Nutrient Classification
+
+A dataset for classification of Banana leaf nutrient deficiencies. The dataset contains raw and augmented versions.  
+The raw dataset contains 5,348 images.  
+Images per class:
+- Boron: 173
+- Calcium: 794
+- Healthy: 1,584
+- Iron: 151
+- Magnesium: 288
+- Manganese: 24
+- Potassium: 381
+- Sulphur: 1,240
+- Zinc: 713
+
+The augmented dataset contains 12,747 images.  
+Images per class:
+- Boron: 1,384
+- Calcium: 1,588
+- Healthy: 1,586
+- Iron: 1,359
+- Magnesium: 1,440
+- Manganese: 1,200
+- Potassium: 1,524
+- Sulphur: 1,240
+- Zinc: 1,426
+
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{sunitha2023fully,
+  title={A fully labelled image dataset of banana leaves deficient in nutrients},
+  author={Sunitha, P and Uma, B and Channakeshava, S and Suresh Babu, CS},
+  journal={Data in Brief},
+  volume={48},
+  pages={109155},
+  year={2023},
+  publisher={Elsevier}
+}
+```
+
+p, sunitha; B, Uma; Babu C S, Suresh ; S, Channakeshava (2025), “Images of Nutrient Deficient Banana Plant Leaves”, Mendeley Data, V2, doi: 10.17632/7vpdrbdkd4.2