Digital Farming: Data Exchange, Artificial Intelligence and Sustainability

Software-based solutions for the digital transformation in agriculture

Agriculture is facing many challenges. Not only climate change, but also the scarcity of resources and the decline in biodiversity require adjustments in agriculture. This is where Fraunhofer IESE comes in and supports digitalization in agriculture with software-based solutions. Digital farming optimizes and automates agricultural work processes, both in the field and in the barn as well as in the office.

The aim is to provide comprehensive support to companies from the direct upstream and downstream agricultural business at all stages of their path to digitalization – be it through the provision of innovative technologies or the implementation of complex software systems or software-based systems that increase their efficiency. By organizing targeted events with agricultural companies, we also create a platform for the exchange of experiences and the formation of a joint network to learn from each other and promote synergies. Dialogue with farmers is also important to us in order to be as practical as possible. This commitment also extends to companies in the agricultural industry, particularly in the upstream and downstream sectors.

Research at the Kaiserslautern site is geared towards generating concrete added value. A particular focus here is on developing innovative solutions that not only increase efficiency, but also create the basis for the emergence of new and sustainable business models in this area. We are committed to establishing digitalization in agriculture as a pioneer for positive change and sustainable development. 

Interoperability and Data Exchange

There are currently numerous different digital solutions in the agricultural sector, which often operate in isolation from one another. One of the primary objectives is to link these diverse solutions together and enable optimal networking. To this end, it is essential to ensure the efficient exchange of data [only available in German] between the systems. However, it is particularly important to ensure the sovereignty of data exchange. After all, only secure and barrier-free data transfer will enable the various agricultural technologies to be used optimally and profitably and enable precise planning of agricultural processes. In agriculture, this interoperability leads to well-founded decisions regarding cultivation planning, resource allocation and harvest times, saves time and achieves a better yield. 

Artificial Intelligence and Data in Agriculture

Artificial intelligence offers a wide range of opportunities in agriculture to increase efficiency, optimize resource consumption or promote sustainable agricultural practices. Research at the Kaiserslautern site focuses on the following points:

  • Autonomous agricultural robotics: We are researching technical developments such as sensor data to establish and design safe autonomous systems [only available in German] in agriculture. The focus is on the trustworthiness and safeguarding of AI models for autonomous driving functions as well as dynamic risk management during autonomous operation. 
  • Data management and data quality: Our core topics include the efficient handling of complex data volumes, including automated analysis in agriculture, also with regard to sensor data. We focus on decision support and environmental monitoring. Our aim is to support companies in the agricultural sector in evaluating the quality of their data, analyzing it in a meaningful way and generating added value from it. On the other hand, we are committed to developing customized, data-driven business models that help our customers to achieve their goals effectively and strengthen their competitiveness.

Digitalization as an Opportunity for More Sustainability in the Long-Term Economy

In order to take account of environmental protection in agriculture, a key focus is on resource conservation. Research concentrates on finding more efficient ways of using pesticides, fertilizers, irrigation and feed. The researchers at Fraunhofer IESE are focusing on three main areas: the digital mapping of nutrient cycles, the automation of documentation, i.e. the optimized process of data collection and reporting, and the development of approaches for identifying and evaluating data quality to ensure that only reliable data is used for automated documentation. The aim is to achieve a balance between productivity and environmental protection for greater sustainability [only available in German].

Projects and Developments


Fraunhofer Lighthouse Project »Cognitive Agriculture (COGNAC)«

Eight Fraunhofer Institutes are researching smart farming for sustainable and productive agriculture. The aim is a data-based »Agricultural Data Space« that optimizes the value chain from production to processing.


Project »X-KIT«

The focus of this project is the networking and support of 36 AI research projects and the promotion of the agricultural domain in GAIA-X. The aim is to make maximum use of synergies between compatible digitization solutions.


Project »AgriDataSpace«

The EU-funded project serves as a preparatory measure to pave the way for a secure European data space in agriculture. The aim is to facilitate the secure exchange, processing and analysis of data in order to promote data-driven innovation and the optimized use of natural resources.

Industrial Projects


Reference Project »Demeter«

The aim of the public Smart Farming EU project is to develop innovative solutions for agriculture based on technologies such as IoT, AI and big data. The tools developed for data evaluation and driving behavior analysis enable well-founded decisions, optimize fuel consumption and reduce vehicle wear for more efficient and sustainable agriculture.


Reference Project »John Deere«

In cooperation with John Deere, the aim was to enable simple data exchange for digital agriculture. Fraunhofer IESE developed efficient software control mechanisms integrated into the SAVE tool.


Reference Project »IC Space«

With Insights Collaboration Space (ICSpace), the John Deere European Technology Innovation Center (ETIC) and Fraunhofer IESE have developed the first collaboration app for interdisciplinary teams working together on data-driven digital services.


Reference Project »Robert Bosch GmbH«

In this project, a flexible software security architecture was developed that enables the use of hardware without specific security functions for security-relevant applications.


Reference Project »Commercial Vehicles«

The public project »New testing concepts for safe software in highly automated commercial vehicles« aims to ensure the safety of highly automated and networked commercial vehicles.


Reference Project »Hitachi Ltd. Success Story«

The safety engineering specialist Fraunhofer IESE worked together with Hitachi Ltd. on issues relating to ensuring safety in the field of autonomous driving.