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Project

AI-Test-Field

Experimental environment for industrial-grade development of semantic environment perception.

Experimental environment for industrial-grade development of semantic environment perception.

  • Duration:

The fusion of data with high spatial and temporal resolution and their interpretation are essential innovation drivers for the realisation of more sustainable processes in plant cultivation. Here, economic potentials go hand in hand with ecological improvements such as resource savings, selective processes and the integration of flowering regions in plant stands and mixed crops. Different environmental conditions, such as the growth stages of the plants, soil properties, the emergence of weeds and weeds by-products, as well as weather conditions and machine influences, have a direct impact on the correctness and availability of sensor data and their interpretation with regard to a semantic environment perception.

AI-based algorithms offer the opportunity to develop robust sensor systems that generate valid and safe operations in a wide range of environmental conditions. The generation of reproducible test scenarios under different environmental conditions is essential for development of these systems. Therefore, an outdoor test environment is being build up in AI-Test-Field. The aim is to carry out autonomous long-term tests to generate sensor data under variable field, weather and plant conditions. In addition to the sensor signals, meta-data will be recorded for the development and evaluation of AI methods.

The project includes the use of different sensor systems (laser scanner, stereo cameras, ToF camera, ultrasonic and radar) as well as the exemplary transfer to real machines for different use cases (row cropping, grassland and bare ground). The contents of AI-Test-Field form an essential basis for the certification of such sensor systems for autonomous field robotics.

Partners

Hochschule Osnabrück, LEMKEN GmbH & Co. KG, Maschinenfabrik Bernard Krone GmbH & Co. KG

Sponsors

BMEL - Federal Ministry of Food and Agriculture

28-D-K1.01A-20

BMEL - Federal Ministry of Food and Agriculture

Publications about the project

Jan Christoph Krause; Jaron Martinez; Henry Gennet; Martin Urban; Jens Herbers; Stefan Menke; Sebastian Röttgermann; Joachim Hertzberg; Arno Ruckelshausen

In: Referate der 43. GIL-Jahrestagung. Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft (GIL-2023), February 13-14, Osnabrück, Germany, Köllen Druck+Verlag GmbH, Bonn, 2/2023.

To the publication

Naeem Iqbal; Mark Niemeyer; Jan Christoph Krause; Joachim Hertzberg

In: 2023 European Conference on Mobile Robots. European Conference on Mobile Robots (ECMR-2023), September 4-7, Coimbra, Portugal, IEEE, 2023.

To the publication

Jan Christoph Krause; Jaron Martinez; Henry Gennet; Martin Urban; Jens Herbers; Stefan Menke; Sebastian Röttgermann; Joachim Hertzberg; Arno Ruckelshausen

In: ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers Annual International Meeting (ASABE-2023), ITSC-Information Technology, Sensors & Control Systems Machine Vision for Agricultural Applications, located at ASABE Annual International Meeting, July 9-12, Omaha, Nebraska, USA, ASABE, 2023.

To the publication