Update README.md

README.md

@@ -36,4 +36,37 @@ configs:
   data_files:
   - split: train
     path: raw/train-*
+license: cc-by-4.0
 ---
+# Pheno4D Point Cloud Segmentation
+
+A dataset for point cloud semantic segmentation of maize and tomato plants. The dataset contains 126 labeled 3D scans across two species, tomato (single label scheme) and maize (dual label scheme: collar-based and tip-based organ boundaries), with per-point labels identifying plant organs (e.g. stem, leaf).
+
+Each sample includes:
+- `points`: an (N, 3) array of x, y, z point coordinates, N varies per scan
+- `mask`: an (N, 1) array of per-point organ labels (primary label scheme)
+- `mask_2`: an (N, 1) array of per-point organ labels (secondary label scheme, maize only; -1 where not applicable)
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+**Note:** this is AgML's first point cloud dataset. Naming follows the `mask` convention used for 2D semantic segmentation datasets in this organization, adapted for 3D point data (stored as `Array2D` with a dynamic first dimension rather than image masks).
+
+## Citation
+
+```bibtex
+@article{schunck2021pheno4d,
+  title={Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis},
+  author={Schunck, David and Magistri, Federico and Rosu, Radu Alexandru and Corneli{\ss}en, Anne and Chebrolu, Nived and Paulus, Stefan and L{\'e}on, Jens and Behnke, Sven and Stachniss, Cyrill and Kuhlmann, Heiner and Klingbeil, Lasse},
+  journal={PLOS ONE},
+  volume={16},
+  number={8},
+  pages={1--18},
+  year={2021},
+  publisher={Public Library of Science},
+  doi={10.1371/journal.pone.0256340}
+}
+```
+
+Original dataset: https://www.ipb.uni-bonn.de/data/pheno4d/
+
+*This dataset was reformatted to match HuggingFace standards as part of AgML's point cloud dataset support.*