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README.md

@@ -44,4 +44,37 @@ configs:
   - split: train
     path: raw/train-*
   default: true
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+size_categories:
+- n<1K
 ---
+# Openear Base Segmentation
+
+This dataset provides real-world RGB imagery of maize crops in field environments, captured at Hongqi Base, Hainan, China. Collected using ground-based platforms with a Raspberry Pi HQ camera during the 2025-2026 growing season, it supports semantic segmentation tasks for agricultural phenotyping in maize fields. The dataset contains raw and augmented versions.
+The raw split contains 919 images with pixel-level mask annotations.  
+
+The augmented split contains 3,859 images with pixel-level mask annotations.  
+
+
+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{fan2026openear,
+  title={OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system},
+  author={Fan, Shaoqi and Li, Guoji and Bahitwa, Revocatus and Jia, Zhiguo and Zhang, Hongwei and Shao, Jinghong and Yu, Qiuying and Chen, Xiaoran and Qian, Yiheng and Xu, Mingchi and Zhu, Linlin and Wang, Hai},
+  journal={Plant Methods},
+  volume={22},
+  pages={26},
+  year={2026},
+  publisher={BioMed Central}
+}
+```
+
+
+*This dataset was reformatted from its original format to match HuggingFace standards.*