English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Meeting Abstract

RAPA: RaspberryPi Automated Phenotyping Array provides low-cost, scalable, automated, high-throughput, in-place phenotyping

MPS-Authors
/persons/resource/persons273392

Wang,  G
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons277163

Noll,  A
IT-Group, Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons86758

Widmer,  C
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons272550

Rowan,  B
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons85266

Weigel,  D
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Wang, G., Noll, A., Widmer, C., Rowan, B., & Weigel, D. (2014). RAPA: RaspberryPi Automated Phenotyping Array provides low-cost, scalable, automated, high-throughput, in-place phenotyping. In 25th International Conference on Arabidopsis Research (ICAR 2014) (pp. 177).


Cite as: https://hdl.handle.net/21.11116/0000-000A-E016-0
Abstract
Today, genetic data is relatively cheap, but phenotypic data is costly in time and resources. Many phenotypes are measured by hand. The full potential of next-generation sequencing will not be realized without high-throughput phenotyping. Several high-throughput phenotyping solutions exist, but they are expensive and often require large infrastructure investments. Here, we present RAPA: the RaspberryPi Automated Phenotyping Array. The system consists of one to arbitrarily many RaspberryPi computing units and cameras, backed by a unified system for control, monitoring, and maintenance. Plants are imaged simultaneously, in place, without handling. Images are then automatically segmented using machine learning based software, and morphometrics are extracted and placed in a web-accessible database. RAPA provides a complete consistent photographic record of all plants monitored, in addition to a unified database of experimental metadata and phenotypic outcomes. RAPA is constructed using open source software, and full specifications will be made available.