Cloud architecture for plant phenotyping research

Abstract

Summary Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.

Code and data availability

The supplied blocks describe a cloud/lambda architecture built on Apache Druid, Mesos, Hadoop, and Spark for plant phenotyping. No public phenotype/trait dataset, plant image or sensor data deposit, authors' analysis code, trained model, or supplement with such assets is mentioned. The URLs present (transPLANT, EPPN202

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