Upscaling (real-time) sensor data for EU-wide monitoring of production and agri-environmental conditions

Sensors are increasingly used to enhance agricultural production e.g. through the assessment of environmental and crop conditions as well as through livestock monitoring. The information value of data collected through sensors can be increased through the analysis of the data in combination with other data sets. Reference data may, e.g. be formed by data sets generated by sensors at other places or by satellite and earth observation data or other data sets reflecting on environmental conditions. Data generated locally through sensors is often more precise, in comparison to global / EU-wide / national / or regional data sets.

The interpretation of local data sets benefits from such supra-regional data sets allowing e.g. for comparison of crop conditions, e.g. as basis for developing approaches to adapt agricultural production to climate change or for market analyses. In addition, there is the possibility to upscale the more detailed through sensors locally generated information through the application of data technologies, allowing to generate a data, information and knowledge base. Such bases can serve as input for analyses to serve the agricultural sector as well as environmental, climate, and wider policy monitoring purposes.

Of particular interest in agricultural production are approaches of real-time data generation and processing allowing for instance to better tailor certain production steps, combine different production steps or operate Internets of Things (IoT). Edge computing can play a key role to facilitate and enhance such sensor-based analyses and production approaches.

Proposals should cover all of the following aspects:

• Development of innovative approaches to use in-situ data collected through sensors used in agricultural production as input to the application of data technologies.
• Development of approaches to analyse the data in real time through processing at the source (edge computing) associated with analytics (including AI) in combination with e.g. earth observation data.
• Development of innovative approaches to benchmark and tailor agricultural production through sensor data sharing at regional level including the development of business models.
• Development of approaches to generate EU-wide data sets through the upscaling of data collected through sensor used in agricultural production (in combination with other data sets, such as satellite data).
• Demonstration of how sensor-generated data can be further capitalised for the development of the agricultural sector, other sectors and the public good (including policy-making and implementation).

Based on a stock-taking analysis, proposals should (also) focus on crops currently covered less by (private sector) sensor developments. Approaches towards livestock monitoring and/or approaches towards monitoring of agri-environmental conditions through livestock data should be considered. Proposals should reflect on different bio-geographic conditions in Europe.

Proposals are expected to demonstrate governance and management structures allowing for a steady adaptation of the work schedule of the projects (like a rolling plan); this is expected to allow to adapt the work to the most recent developments and innovations in the field of sensors and sensor data in the public and private domain.

Proposals are expected to reflect on possibilities to interlink (interim) project results or parts of them to the functioning of the forthcoming common European agriculture data space and/or the common European data space for research and innovation, the European Open Science Cloud in cooperation with the European Commission. The potential of internet of things (IoT) technologies should be considered.

The possible participation of the JRC in the project will ensure that the approach proposed will be compatible with and improve the tools used and or developed at the European Commission.

In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement.