Optimization of Agricultural Processes

Fraunhofer Lighthouse Project COGNAC

Automated machines determine the image of highly productive agriculture. Increasingly larger working widths and speeds as well as increasing degrees of automation have led not only to a significant rise in productivity, but also to heavier machines. Fertilization, excessive use of herbicides, pesticides, and fungicides, or various types of genetically modified seeds also cause permanent damage to the biosphere. Organic agriculture has evolved as an alternative. Here, the focus is on resource-friendly farming, and productivity losses are accepted deliberately.

The digitalization, automation and electrification of agricultural processes offer numerous starting points for finding the right balance between sustainability and productivity. The Fraunhofer lighthouse project »Cognitive Agriculture« aims to automatically collect data about complex interrelationships in farming and, based on that, to support decision-making processes in the value network.


Data Analysis for Better Work Processes

In order to achieve an improvement in agricultural processes, COGNAC is divided into three innovation areas:

  • Biosphere Monitoring / Novel Sensor Technology:
    Automated interpretation and decision support based on high-resolution measurement data from airborne or ground-based systems.
  • Digital Ecosystem / Agricultural Data Space:
    Open data exchange in an agriculture-specific, digitally networked ecosystem that enables the use and linking of complex agricultural data in secure data spaces.
  • Autonomous Field Robots / Innovative Automation Concepts:
    Autonomous field robotics for plant-specific field work as well as robot-guided sensor platforms with specific sensor systems.


Our Tasks in the Project

In addition to seven other Fraunhofer Institutes and four Fraunhofer Alliances, our divisions »Mathematics for Vehicle Engineering« and »Optimization« are working on the lighthouse project.

Our work is dedicated to the modelling, simulation and optimization of agronomic processes (e.g. growth and yield of wheat). An important goal is to identify correlations and influencing factors and to derive recommendations for action. One example of this is robust harvest campaign planning: we continuously monitor updated ripening data and weather forecasts for the fields to be harvested, enabling us to plan machinery and staff with foresight. Robust models and algorithms reduce drying and fuel costs while improving food quality and customer satisfaction. These questions benefit from our many years of cooperation with agricultural machinery manufacturers.