Abstract

Agriculture is a vital industry, not only important for nourishment, but also a key determinant of health, economic and political stability, employment, business and biological ecosystems and society. Because of its importance, most attention is focused on productivity but it is essential to have a global view in order to address environment sustainability problems. EUXDAT proposes an e-Infrastructure, which addresses agriculture, land monitoring and energy efficiency for a sustainable development, as a way to support planning policies. In order to do so, EUXDAT addresses the problems related to the current and future large amount of heterogeneous data to be managed and processed. EUXDAT builds on existing mature components for solving them, by providing an advanced frontend, where users will develop applications on top of an infrastructure based on HPC and Cloud. The front end provides monitoring information, visualization, different parallelized data analytic tools and enhanced data and processes catalogues, enabling Large Data Analytics-as-a-Service. EUXDAT will include a large set of data connectors (UAVs, Copernicus, field sensors, etc.), for scalable analytics. As for the brokering infrastructure, EUXDAT aims at optimizing data and resources usage. In addition to a mechanism for supporting data management linked to data quality evaluation, EUXDAT proposes a way to orchestrate tasks execution, identifying whether the best target is a HPC center or a Cloud provider. It will use monitoring and profiling information for taking decisions based on trade-offs related to cost, data constraints, efficiency and resources availability. During the project, EUXDAT will be in contact with scientific communities, in order to identify new trends and datasets, for guiding the evolution of the e-Infrastructure. The final results of the project will be an integrated e-Infrastructure which will encourage end users to create new applications for sustainable development.

Abstract

BACCHUS intelligent mobile robotic system promises to reproduce hand harvesting operations, while at the same time taking the manual legwork out by autonomously operating on four different levels:

1. Performing robot navigation with quality performance guarantee in order to inspect the crops and collect data from agricultural areas through embedded sensorial system.
2. Performing bi-manual harvesting operations with the needed finesse using a modular robotic platform,
3.  Employing additive manufacturing for adjusting the robot gripper to the geometry of the different crops,
4. Presenting advanced cognitive capabilities and decision making skills.

The envisioned system will be demonstrated and evaluated in the challenging vineyard environment while inspecting different types of vines and harvesting bunches of grapes of different varieties in a human-like manner.

Abstract

STARGATE’s contribution beyond state-of-the-art in applied climatic data solutions is the implementation of analytics models to support local and regional policy formulation and implementation related to mitigation on microclimate changes. Currently, policy making organizations predominantly utilize their own data, typically limited to their own jurisdiction/administrative area. However, once the policy development process expands beyond the traditional approach, there need for global data will emerge. The addition of national, European and even global reference data sets for comparative analysis including meteorological data, climatic analysis, and satellite data sources are needed to improve decision making processes. The studies at landscape scale are required to understand the ecological processes.

The focus will be on Climate Smart Agriculture (CSA). The benefits of applying agri-environment-climate technical solutions will be extensively studied to achieve sustainable agricultural development at landscape level. This can be achieved by supporting farm management modernization and at the same time getting to know the underlying ecological factors that shape the farming landscape. STARGATE will leverage access to these data while its climatic platform will foster easy and affordable adoption by policy making bodies. This simple approach is effective in the visualization of data and can support decision making in policy development.   Unlike algorithmic simulation and modeling, visualization leaves the actual decision and assessment to a human user and thus provides an extra quality assurance before actual policy decisions are made. STARGATE provides innovative components for visualization of big data with a particular emphasis on geospatial visualization and advanced, dynamic charting.

Abstract

The NAIADES project envisions the transformation of urban water management through automated and smarter water resource management and environmental monitoring, achieving a high level of water services for both residential and commercial consumers, exploiting the efficient use of physical and digital components of water ecosystem. NAIADES relies and builds upon various types of big data collected from different water monitoring and control systems in Europe, in order to: (i) Establish more efficient water consumption in both retail and commercial environments. (ii) Generate increased confidence of water consumers. (iii) Measure the water quality in residential buildings, offices and public infrastructures (mall, hospital). (iv) Assure the safety and reliability through the detection of warning signs in near real time and other monitoring systems. (v) Enhance public awareness on water consumption and usage savings, and promote user engagement in water conservation activities through personalized persuasive feedback and recommendation services provided to the NAIADES App Users. NAIADES aims to provide multidimensional intelligence on the water ecosystem through the introduction of Artificial Intelligence (AI) technologies:

• Situational Intelligence by collecting real-time data from the buildings as they are in operation and analysing them in Spatial, Temporal and Nodal dimensions.
• Operational intelligence by using the power of data and its capability to extract the right information at the right time to provide insight into water infrastructure operation and improve the effectiveness of maintenance activities.
• Asset intelligence by building digital twins that represent physical systems using the continuous data streams produced from various sub-systems in buildings will help OEMs.