ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture | Zendy

Nicolás Gaggion | Zendy; Federico Ariel | Zendy; Vladimir Daric | Zendy; Éric Lambert | Zendy; Simon Legendre | Zendy; Thomas Roulé | Zendy; Alejandra Camoirano | Zendy; Diego H. Milone | Zendy; Martín Crespi | Zendy; Thomas Blein | Zendy; Enzo Ferrante | Zendy

Open Access

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Author(s) -

Nicolás Gaggion,

Federico Ariel,

Vladimir Daric,

Éric Lambert,

Simon Legendre,

Thomas Roulé,

Alejandra Camoirano,

Diego H. Milone,

Martín Crespi,

Thomas Blein,

Enzo Ferrante

Publication year - 2021

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giab052

Subject(s) - computer science , convolutional neural network , deep learning , artificial intelligence , phenomics , segmentation , root (linguistics) , pattern recognition (psychology) , consistency (knowledge bases) , process (computing) , biology , genomics , biochemistry , linguistics , philosophy , genome , gene , operating system

Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.

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