Cloud architecture for digital phenotyping and automation.

Abstract:

Digital phenotyping presents a very important tool for scientists to measure with high accuracy the effects of external phenomena on plant development. Plant phenotyping is mainly based on imaging techniques. However, the number of images and parameters used to store and treat these parameters are continuously growing. Consequently, the high-throughput of data and the need of specific treatment in real or near real-time requires a large quantity of resources. Moreover, the increasing amount of particular phenotyping case studies needs the development of specific application. Cloud architectures offers means to store a wide range of numerous data and host a large quantity of specific software to process these data. In this paper, we propose a new approach that shows how logic synthesis works to match digital phenotyping need and cloud possibilities in a lambda cloud architecture in order to store and treat this important amount of data in real time. We also suggest a data platform allowing to host applications and access to the stored data within the lambda architecture. The present application platform allows to use several frameworks with a fine-grained resource use of the cluster. Finally, we develop a case study in a controlled environment system (growth chamber) where we grow basil plants.

Keywords: cloud; lambda architecture; digital phenotyping; 3D plant model; phytotron;application platform

                

Citation

MLA Debauche, Olivier, et al. "Cloud architecture for digital phenotyping and automation." 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech). IEEE, 2017.
 
ISO 690 DEBAUCHE, Olivier, MAHMOUDI, Said, MANNEBACK, Pierre, et al. Cloud architecture for digital phenotyping and automation. In : 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech). IEEE, 2017. p. 1-9.
 
APA Debauche, O., Mahmoudi, S., Manneback, P., Massinon, M., Tadrist, N., Lebeau, F., & Mahmoudi, S. A. (2017, October). Cloud architecture for digital phenotyping and automation. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) (pp. 1-9). IEEE.
 
Chicago Debauche, Olivier, Said Mahmoudi, Pierre Manneback, Mathieu Massinon, Nassima Tadrist, Frédéric Lebeau, and Sidi Ahmed Mahmoudi. "Cloud architecture for digital phenotyping and automation." In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), pp. 1-9. IEEE, 2017.
 
Harvard Debauche, O., Mahmoudi, S., Manneback, P., Massinon, M., Tadrist, N., Lebeau, F. and Mahmoudi, S.A., 2017, October. Cloud architecture for digital phenotyping and automation. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) (pp. 1-9). IEEE.
 
Vancouver Debauche O, Mahmoudi S, Manneback P, Massinon M, Tadrist N, Lebeau F, Mahmoudi SA. Cloud architecture for digital phenotyping and automation. In2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) 2017 Oct 24 (pp. 1-9). IEEE.
 
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