Datasets:

ngaggion
/

UnsupervisedRootVideos

language:
  - en
license: cc0-1.0
task_categories:
  - image-segmentation
tags:
  - plant root phenotyping
  - biology
  - plant science
arxiv: 2504.14736
configs:
  - config_name: all
    data_files:
      - split: train
        path: '**/*'

ChronoRoot Dataset without annotations and full time series

Project Page | Paper | GitHub

Dataset Description

Dataset Summary

This dataset contains the complete time series from where the annotations were extracted, to train weakly supervised models when combining these with the annotated frames.

Data Structure

Raw Images: 3280 x 2464 infrared images

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{10.1093/gigascience/giag018,
    author = {Gaggion, Nicolás and Boccardo, Noelia A 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 Santoro, Luciano J and Accavallo, Franco and Villarreal, Santiago Nahuel and Pereyra-Bistrain, Leonardo I and Benhamed, Moussa and Crespi, Martin and Ricardi, Martiniano María and Petrillo, Ezequiel and Blein, Thomas and Ariel, Federico and Ferrante, Enzo},
    title = {ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping},
    journal = {GigaScience},
    pages = {giag018},
    year = {2026},
    month = {02},
    abstract = {Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs.ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.https://chronoroot.github.io},
    issn = {2047-217X},
    doi = {10.1093/gigascience/giag018},
    url = {https://doi.org/10.1093/gigascience/giag018},
    eprint = {https://academic.oup.com/gigascience/advance-article-pdf/doi/10.1093/gigascience/giag018/67174649/giag018.pdf},
}

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}
}