Publication
A Tool Set for Development and Testing of Perception Systems in Agriculture
Jan Christoph Krause; Sebastian Röttgermann; Alexander Tauber; Jens Herbers; Naeem Iqbal; Mark Niemeyer; Hannes Hollmeier; Thorben Boße; Simon Schirk; Robin Hilker; Stefan Stiene; Martin Atzmueller
In: VDI Wissensforum GmbH (Hrsg.). VDI-Berichte. International Conference Agricultural Engineering (LAND.TECHNIK-2025), 82th International Conference on Agricultural Engineering LAND.TECHNIK AgEng 2025, November 7-8, Hannover, Germany, VDI Verlag, Düsseldorf, 2025.
Abstract
Reliable perception of the surroundings of an agricultural machine is a core requirement to
ensure safe and efficient operation of automated agricultural machinery. The perception
performance depends on factors like weather, vegetation, and operational conditions. When
developing perception systems, performance must be evaluated for all operational conditions.
Since this involves combinations of several environmental parameters, the design,
management, and evaluation of relevant test scenarios is complex.
Within the research project AI-TEST-FIELD, we build a comprehensive tool set to record
comparable sensor data of different weather and vegetation conditions and connect that with
an evaluation pipeline; with that, we can enhance that data with auto-generated ground truth
and predictions from perception algorithms.
Our toolset enabled us to record a broad data set with comparable test scenarios under
different environmental conditions, allowing us to investigate the influence of weather and
plants on the performance of sensor systems.
