The Design Environment is a graphical user interface (GUI) with the aim of helping users to get in touch with the PHYSICS solution framework, providing the tools and a GUI to simplify the development, testing and management of FaaS applications, all in one place.

The Design Environment is a graphical user interface (GUI) with the aim of helping users to get in touch with the PHYSICS solution framework, providing the tools and a GUI to simplify the development, testing and management of FaaS applications, all in one place.

The GUI is a downloadable packaged solution running as docker container on the user client device. Such solution gives to the user the flexibility, at the start up of the project, to leverage the user resources and don’t necessary rely on the cloud environment, with an approach of EDGE computing[1] architecture.

The GUI is divided in two main sections: the first one is dedicated to the Node-RED[2] application that provides a browser-based editor that makes easier to wire together flows using the wide range of available nodes, even custom PHYSICS’s nodes; the second one is the Admin Panel where the tools for managing the deployed Node-RED flows are located.

In the “Admin Panel” the user can proceed to the build of the deployed Node-RED flow to make it ready for the execution in the PHYSICS platform. Then, in the “Test” section, the flow can be tested from both logical and performance point of views. The last section (“Graph”) is dedicated to the creation of the graph where the user can define an application that includes one or more flows.

The application also gives to the user the possibility to load the (not generated) custom images into the integrated Node-RED environment, which can also be located in a user’s custom repository.

The Design Environment integrates a login system based on Keycloak[3], which provides an all-in-one solution to manage the user’s Single Sign-On (SSO) to the application, i.e. the capabilities of authentication, authorization and segregation of the user workspace. The SSO feature enables the future opportunity of enforcing the security of the PHYSICS resources.

The application interacts with the other components of the PHYSICS environment, to enable the build, testing and management of the related flow. In Figure 2, a high-level diagram of the interactions of the GUI with the other PHYSICS components is represented.

[1] Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. (https://en.wikipedia.org/wiki/Edge_computing)

[2] Node-RED (https://nodered.org/)

[3] Keycloak (https://www.keycloak.org/)

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CybeleTech is a French SME, established in 2011, that aims at developing the use of numerical technologies in agriculture. The core products of CybeleTech are based on numerical simulations of plant growth through dedicated biophysical models and machine learning methods extracting knowledge on processes through large databases. These new technologies can bring added values at different stages of the agriculture and food chain: plant breeding, optimization of cultural practices, forecasting yields and production at large scale for insurance, optimization of first transformation processes. These algorithms and the associated data are resource consuming, in terms of computation and storage.

In Physics CybeleTech is leading the smart-agriculture use case dedicated to optimization of deployment of crop monitoring for decision aid solutions on Edge for crops needing precise and quasi real time management like greenhouses. To achieve better use of theses complex cultivation system, growers need to anticipate how the setup of the greenhouse devices, such as heating, CO2 generator or irrigation system, will impact plant development and then the operator’s gross margin.

As depicted in the image below, the decision support system developed by Cybeletech enables to connect to the greenhouse for collecting data measured by various sensors. These data are transferred to Cybeletech servers where there are pre-processed and stored in appropriate databases. Agronomic models are fed with these data, allowing to explore different management scenarios in term of production cost and outcome. Furthermore, to ensure realistic crop growth simulation, agronomic models’ parameters must be adjusted through calibration procedure with high computational cost.

The main challenges faces are: 1) To ensure a continuous flow of data between greenhouse and the databases with high resilience to connection failure; 2) To ease the deployment of the DSS in very different and specific infrastructures; 3) To improve the computational efficiency for optimization and model calibration procedures, while reducing the costs for growers.

Edge computing will allow to store the environmental data collected by the sensors at the most local level, thereby limiting the risk of data loss due to unstable internet connection. Moreover, it will allow to run short-term simulation based on these local data directly in the greenhouse ensuring autonomous, near real-time greenhouse management optimization. On the other hand, cloud computing and FaaS will allow to improve computational efficiency, while reducing the costs for growers.

The PHYSICS project will provide precious tools to facilitate the uptake of those two architectures and ensure the continuity between them.

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