Icon Agri-Gaia

Practical example and current challenges

  • Of all the sectors of our economy, the biggest productivity gains in the last 70 years have been in agriculture. Digitalisation will continue to drive productivity and the shape production processes in agriculture.
  • After all, agriculture is facing a variety of challenges: Increasing price pressure and growing consumer interest in sustainably produced and cheap food, as well as climate change, are leading to necessary increases in efficiency and effectiveness in production and continuous adaptation to new framework conditions in agricultural production.
  • Digitisation makes it possible to trace food, to treat plants more precisely or to obtain information on the state of health of animals. The new importance of data processing for the improvement of production processes is currently presenting medium-sized agricultural players with major obstacles.
  • It is often difficult to link the data, because the exchange of machine data across manufacturers already requires great standardization efforts. Access to open data, self-generated data and documentation of the processes performed is often difficult.
  • Agri-Gaia is designed as a decentralised infrastructure for the exchange of data and algorithms in agriculture. GAIA-X is used as a basic infrastructure to implement, for example, identity management and semantic description of services and data.
  • A central platform in the Agri-Gaia architecture provides these services and offers basic data and algorithms that are relevant in agriculture and on which a wide range of products can be based.
  • Through GAIA-X based standardization, Agri-Gaia enables farmers to move their data freely between different cloud platforms and use them economically themselves.

What added value does the "GAIA-X project" offer?

  • The Agri-Gaia project creates an AI ecosystem for the SME agricultural and food industry based on GAIA-X. For this purpose, an innovative B2B platform will be implemented, which provides industry-specific adapted AI building blocks as easy-to-use modules and brings together users and developers of AI algorithms. Agri-Gaia closes the circle from sensor data acquisition on the agricultural machine, training of the algorithms on appropriate servers and continuous updat-ing/optimization of the algorithms. Appropriate interfaces and standards are being developed so that a manufacturer-independent infrastructure for the exchange of data and algorithms is created.
  • This ecosystem will be built on the basis of the GAIA-X infrastructure, which ideally meets the requirements of the industry and the Agri-Gaia ecosystem in terms of data sovereignty, decentralization/multi-cloud and edge support and service delivery.
  • Through use cases, Agri-Gaia demonstrates how this system enables the industry to address the key issues of efficient and sustainable agriculture. The application-strong Agri-Gaia consortium (with many national and international market leaders) and a vibrant and growing stakeholder ecosystem supported by Agri-Gaia ensure that GAIA-X is directly relevant to the market in this important German industry.
  • Through GAIA-X, the emerging Agri-Gaia platform will gain increased presence to achieve further market relevance and faster market penetration, as well as to reach more platform users. The scaling of the platform will be facilitated.
  • GAIA-X offers technologies such as cross-industry identity management and semantic description of data formats, providing the necessary basic infrastructure to enable decentralized and interoperable data management in agriculture.
  • GAIA-X also provides access to more DSGVO-compliant data and strengthens the confidence of farmers of medium-sized farms in the security of their data.
  • GAIA-X makes it possible to merge training data sets across industries, so that AI systems can recognize interrelationships and optimize the corresponding process chains.
  • In addition, GAIA-X creates a network of companies, research institutes and universities that promotes the exchange of information on AI topics throughout the agribusiness sector, thus creating new B2B business processes based on the Agri-Gaia infrastructure in agriculture, including upstream and downstream economic sectors.

Use Case Team

  • Jochen Fehse - Robert Bosch GmbH
  • Dr. Stefan Stiene - Competence Center Smart Agriculture Technologies, DFKI GmbH
  • AgBrain GmbH
  • Agrotech Valley Forum e.V.
  • Amazonen-Werke H.Dreyer GmbH & Co. KG
  • CLAAS E-Systems GmbH
  • DFKI GmbH
  • University of Applied Sciences Osnabrück
  • Josef Kotte Landtechnik GmbH
  • Maschinenfabrik Bernard Krone GmbH & Co. KG
  • Robert Bosch GmbH
  • University Osnabrück
  • Wernsing Feinkost GmbH