At this year’s Field Robot Event at the University of Maribor in Slovenia, our robot impressively outperformed 14 other competitors in the computer vision-focused Task 3. The task determined the accuracy of the competing robots‘ recognition systems on a series of images of people, deer, and other objects. We also took 2nd place in the Freestyle category and 4th place in the Smart Irrigation task. We would like to thank our sponsors who supported our participation in the competition with financial resources and hardware. For a deep dive into our methodology, we will soon publish our FRE 2023 proceedings on this blog.

Last year, we published our dataset and models for image recognition during the online Field Robot Event 2021. Our blog post ended with a call for cooperation in creating a dataset and AI models for this year’s event, which has since taken place at DLG Feldtage in June. Team Carbonite from the Überlingen Students’ Research Center (SFZ Überlingen) approached and we subsequently worked together on building a realistic image dataset. As a nod to the two teamnames, this dataset shall henceforth be known by the name “Carbonaro”.

When we upgraded our robot Beteigeuze with entirely new electronic components, we decided on nVidia’s Jetson Nano platform as the robot’s primary computer. The Jetson Nano is an ARM based quad-core System-on-a-Chip (SOC) that features CUDA acceleration for Deep Learning models. As a test, we tried deploying the model that we used during last year’s Virtual Field Robot Event on the new hardware platform. Even though that model was trained for simulation, we hope that models of the same or similar architecture will be of value for us in this year’s event. The model is a ResNet50 from PyTorch’s torchvision.

FRE 2021 is over! It’s been a great week seeing all of the other teams again and competing with one another. We are grateful for the opportunity we had in building the competition environment and shaping this year’s Field Robot Event! We were especially pleased with the organizers’ decision to include realistic 3D plant models for the weed detection task. For a long time, simple color-based detectors were sufficient to solve the detection tasks at the Field Robot Event. However, such approaches are not adequate to perform a plant recognition task in a realistic scenario. On the real field, robots will have to distinguish between multiple species of plants — all of which are green — in order to perform weeding, phenotyping and other tasks. Deep learning has been around for a few years now and seems to be the most applicable tool to tackle this challenge.