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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.
