Enhance ImAg4Wheat dataset card with metadata, links, and citation

This Pull Request enhances the `ImAg4Wheat` dataset card by:

- Adding `task_categories` (`image-classification`, `object-detection`, `image-segmentation`), `license` (`mit`), `language` (`en`), and relevant `tags` (`agriculture`, `wheat`, `crop`, `vision`) to the metadata for improved discoverability.
- Including direct links to the associated paper ([FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data](https://huggingface.co/papers/2509.06907)), the project page (https://fomo4wheat.phenix-lab.com/), and the GitHub repository (https://github.com/PheniX-Lab/FoMo4Wheat) in the content section.
- Adding a note about the dataset's full public availability status, as mentioned in the GitHub README.
- Providing the BibTeX citation for proper attribution.

These additions make the dataset card more comprehensive and user-friendly.

README.md

@@ -1,2 +1,36 @@
+---
+task_categories:
+- image-classification
+- object-detection
+- image-segmentation
+license: mit
+language:
+- en
+tags:
+- agriculture
+- wheat
+- crop
+- vision
+---
+
 # ImAg4Wheat dataset
-To enable the training of the wheat image foundation model, we aggregated a wheat image dataset of the largest scale and of the most diverse to date with significant international effort, involving 10 countries and 30 institutions worldwide, with images comprising over 2000 modern and contemporary wheat genotypes cultivated under more than 500 distinct environmental conditions, covering the full growth cycle from emergence to maturity, and spanning over a decade (2010-2024). This results in more than 2,500,000+ wheat images
+
+[Paper](https://huggingface.co/papers/2509.06907) | [Project Page](https://fomo4wheat.phenix-lab.com/) | [Code](https://github.com/PheniX-Lab/FoMo4Wheat)
+
+To enable the training of the wheat image foundation model, we aggregated a wheat image dataset of the largest scale and of the most diverse to date with significant international effort, involving 10 countries and 30 institutions worldwide, with images comprising over 2000 modern and contemporary wheat genotypes cultivated under more than 500 distinct environmental conditions, covering the full growth cycle from emergence to maturity, and spanning over a decade (2010-2024). This results in more than 2,500,000+ wheat images.
+
+**Note**: The complete dataset will be made publicly available after the peer-review process of the associated paper is completed.
+
+## Citation
+
+If you use our project in your research or wish to refer to the results of the project, please use the following BibTeX entry.
+
+```bibtex
+@article{2025FoMo4Wheat,
+  title={FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data}, 
+  author={Bing Han, Chen Zhu, Dong Han, Rui Yu, Songliang Cao, Jianhui Wu, Scott Chapman, Zijian Wang, Bangyou Zheng, Wei Guo, Marie Weiss, Benoit de Solan, Andreas Hund, Lukas Roth,  Kirchgessner Norbert, Andrea Visioni, Yufeng Ge, Wenjuan Li, Alexis Comar, Dong Jiang, Dejun Han, Fred Baret, Yanfeng Ding, Hao Lu and Shouyang Liu},
+  journal={arXiv:2509.06907},
+  year={2025}
+  note={contact：Shouyang Liu (shouyang.liu@njau.edu.cn)，Hao Lu (hlu@hust.edu.cn)，Yanfeng Ding (dingyf@njau.edu.cn)}
+}
+```