Field Robot Event 2022

We are happy to inform you that we will conduct a Field Robot Event in 2022. Although the pandemic situation continues we do not want to give up. After some discussions we want to offer a hybrid version of the event: a mix of a virtual and field contest! We also will return to the DLG Field Days and prepare everything to be controlled from and on the DLG site. Therefore, robot teams are invited to participate without travelling (virtual event) or come physically as usual together with your own robot (field event). More details will be available soon. Save the date: 14th to 16th of June 2022 in Kirschgartshausen/Mannheim, Germany.

Due to the unpredictable pandemic situation there will be perhaps special rules coming up or even a cancellation. Therefore, please don’t blame us for any inconveniences.

Kamaro reveals winning code!

One of the goals of the field robot event is to learn from each other. We are therefore very happy with Kamaro’s initiative to make some of their software public. We expect this to boost our community and encourage collaboration and exchange between teams. It also helps aspiring participants to get started.

Kamaro has published their winning code for crop row navigation and their code for object detection using deep learning.

Which team is next?

Event to be streamed live on DLG’s digital platform


The Field Robot Event is a partner of the DLG Feldtage event. The DLG is streaming this event live on its digital platform.

Information on how to participate:
• Attending this virtual event is free of charge and requires a simple registration on DLG’s digital platform.
• Registration is open from June 7.
• The link to the registration is available here.

Important updates for teams

  • The containers for the field robot event are now available at Github! Further information can be found in the README.md.
  • The ground texture has been updated. It does not longer contain images of weeds.
  • Changes in the task:
    • All tasks: The starting will always be on the right side of the field. (first turn left)
    • Task 2: A file with the code of the path pattern to be followed is made available to robot at the start.
    • Task 3: Assessment differentiates more clearly between detection during run and classification and accuracy of mapped objects.