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README.md

@@ -13,10 +13,15 @@ datasets:
 
 # ChronoRoot 2.0: Tomato Segmentation Model
 
-This is a pre-trained nnU-Net v2 model for root segmentation in Tomato images.
+This is a pre-trained nnU-Net v2 model for root segmentation in Tomato images. This model is part of the ChronoRoot 2.0 framework for high-throughput phenotyping of plant root systems.
 
-### How to use
-```python
-from nnunet_wrapper import nnUNetv2
-model = nnUNetv2.from_pretrained("ngaggion/ChronoRoot2-Tomato")
-model.predict_single_image("root_photo.png", "mask.png")
+For more information about the ChronoRoot 2.0 framework and its applications, please refer to the [ChronoRoot 2.0 GitHub repository](https://github.com/ChronoRoot/ChronoRoot2) or its associated [website](https://chronoroot.github.io/).
+
+Please cite the following paper if you use this model in your research:
+
+@article{gaggion2026chronoroot,
+  title={ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping}, 
+  author={Nicolás Gaggion and Noelia A. Boccardo and Rodrigo Bonazzola and María Florencia Legascue and María Florencia Mammarella and Florencia Sol Rodriguez and Federico Emanuel Aballay and Florencia Belén Catulo and Andana Barrios and Luciano J. Santoro and Franco Accavallo and Santiago Nahuel Villarreal and Leonardo I. Pereyra-Bistrain and Moussa Benhamed and Martin Crespi and Martiniano María Ricardi and Ezequiel Petrillo and Thomas Blein and Federico Ariel and Enzo Ferrante},
+  journal={arXiv preprint arXiv:2504.14736},
+  year={2026}
+}