Abstract
The rise of high-throughput phenotyping (HTP) has led to a dramatic
increase in the ability to rapidly – and accurately – phenotype
various organisms including plants. However, methods for efficiently
managing, processing, analyzing, and sharing HTP data have not caught up
to this new-found ability to collect big data which in turn introduces a
whole host of new challenges. To address these, we have architected and
implemented a multi-faceted infrastructure of webservices to further
unify and automate the entire data collection process. We have
integrated CyVerse and Slack into our IoHTP, two commonly used tools
within plant science labs. CyVerse is a cloud-based data storage &
management solution and Slack allows for bilateral instant-message
communication with the HTP machines to keep researchers in touch with
their autonomous experiments. Next, we are also developing our own
website for administering jobs remotely to any number of (possibly
geographically distributed) HTP machines. This innovative open-source
approach has the potential to further advance high-throughput
phenotyping worldwide by allowing interdisciplinary experts, namely in
the plant and computer sciences, to collaborate more effectively and
efficiently.