ngaggion/ChronoRoot2-Tomato
Image Segmentation
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ChronoRoot nnUNet Dataset
Project Page | Paper | GitHub
Dataset Description
Dataset Summary
This dataset contains 913 infrared images of Arabidopsis thaliana seedlings and 480 infrared images of Tomato seedlings with expert annotations for six distinct structural classes, developed for training the ChronoRoot 2.0 segmentation model. The dataset includes raw infrared images and their corresponding multi-class segmentation masks.
Supported Tasks
- Image Segmentation: Multi-class segmentation of plant structures.
- Plant Phenotyping: Analysis of root system architecture and plant development.
Classes
The dataset includes annotations for six distinct plant structures:
- Main Root (Primary root axis)
- Lateral Roots (Secondary root formations)
- Seed (Pre- and post-germination structures)
- Hypocotyl (Stem region between root-shoot junction and cotyledons)
- Leaves (Including both cotyledons and true leaves)
- Petiole (Leaf attachment structures, not available in Tomato annotations)
Data Structure
- Raw Images: 3280 x 2464 infrared images
- Segmentation Masks: Multi-class masks in
.nii.gzformat
Dataset Creation
Images were captured using the ChronoRoot hardware system, featuring:
Annotations
- Annotation Tool: ITK-SNAP
- Annotators: Expert biologists
- Verification: Multi-stage quality control process
Personal and Sensitive Information
This dataset contains no personal or sensitive information.
Additional Information
Licensing Information
The dataset is released under Creative Commons Zero (CC0).
Citation Information
If you use this dataset or the platform, please cite the ChronoRoot 2.0 paper:
@article{gaggion2025chronoroot,
title={ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping},
author={Gaggion, Nicolás and Bonazzola, Rodrigo and Legascue, María Florencia and Mammarella, María Florencia and Rodriguez, Florencia Sol and Aballay, Federico Emanuel and Catulo, Florencia Belén and Barrios, Andana and Accavallo, Franco and Villarreal, Santiago Nahuel and Crespi, Martin and Ricardi, Martiniano María and Petrillo, Ezequiel and Blein, Thomas and Ariel, Federico and Ferrante, Enzo},
journal={arXiv preprint arXiv:2504.14736},
year={2025}
}
Original ChronoRoot citation:
@article{gaggion2021chronoroot,
title={ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture},
author={Gaggion, Nicol{\'a}s and Ariel, Federico and Daric, Vladimir and Lambert, Eric and Legendre, Simon and Roul{\'e}, Thomas and Camoirano, Alejandra and Milone, Diego H and Crespi, Martin and Blein, Thomas and others},
year={2021},
publisher={Oxford University Press}
}
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
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Number of rows:
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