Cloud architecture for plant phenotyping research

Abstract

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.

Crop phenotyping in a context of global change: What to measure and how to do it

“…org;Paszke et al, 2019). Moreover, cloud strategies can be incorporated into plant phenotyping to manage the tremendous amount of data generated; these strategies can be used at different levels, from basic data storage and processing (Roy et al, 2017;Samie et al, 2019;Yang and Sun, 2019) through to fully cloud-based infrastructure (Debauche et al, 2017(Debauche et al, , 2020.…”

“…The use of open source deep learning frameworks allows use of a high‐level programming interface where algorithms and models can be developed and tested more easily and reliably: see Caffe (http://caffe.berkeleyvision.org; Jia et al, 2014), MXNet (http://mxnet.apache.org; Chen et al, 2015), TensorFlow (http://www.tensorflow.org; Abadi et al, 2016), MATLAB for deep learning (http://www.mathworks.com/solutions/deep-learning.html; Kim, 2017), and PyTorch (http://www.pytorch.org; Paszke et al, 2019). Moreover, cloud strategies can be incorporated into plant phenotyping to manage the tremendous amount of data generated; these strategies can be used at different levels, from basic data storage and processing (Roy et al, 2017; Samie et al, 2019; Yang and Sun, 2019) through to fully cloud‐based infrastructure (Debauche et al, 2017, 2020).…”

A Cloud Pub/Sub Architecture to Integrate Google Big Query with Elasticsearch using Cloud Functions

“…We mainly faced storage problems in the past, but now, storing large amounts of information is no longer particularly difficult [4]. Although the storage of large amounts of data is still expensive on many occasions, to solve this problem, we have cloud solutions [5]. A good infrastructure allows us to store and maintain data, but it is very little use without the right tools to access it [6].…”

Internet of Things Learning: a Practical Case for Smart Building automation

“…[29] IV. MATERIEL & SOFTWARE Our demonstration hardware is composed of three material package:• Smart Home comprises a Smart Home Automation, a sensor of commercial and Do-it-Yourself sensors which communicate principally with Wi-Fi Protocol.• Smart Building is based on independent shoe boxes in which use cases are implemented.…”