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README.md

@@ -25,4 +25,37 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: mit
+task_categories:
+- image-classification
+size_categories:
+- 1K<n<10K
 ---
+# Grassfield Weed Classification
+
+Grass-field weed patches released with a two-stage weed identification method. Sub-frames cut from images of grass fields are labelled as grass, irregular weed (white clover, Trifolium repens) or regular weed (wild lettuce, Lactuca virosa); the published split has 4,776 sub-frames (3,182 grass, 1,193 clover, 401 lettuce). Larger region frames (229) carry the same three classes and are included too, marked by a frame_type column, with the published train/test split. Images were taken with two cameras under sunny, cloudy and shaded conditions at different locations. The dataset contains 5,005 images across 3 classes: grass, white_clover, wild_lettuce.  
+Images per class:
+- grass: 3,295
+- white_clover: 1,275
+- wild_lettuce: 435
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+The original train/test/val split has been preserved in the `split` column.
+
+## Citation
+
+```bibtex
+@article{calderaracea2024two,
+  title={A two-stage deep learning strategy for weed identification in grassfields},
+  author={Calderara-Cea, Felipe and Torres-Torriti, Miguel and Auat Cheein, Fernando and Delpiano, José},
+  journal={Computers and Electronics in Agriculture},
+  volume={225},
+  pages={109300},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+
+*This dataset was reformatted from its original format to match HuggingFace standards.*