As our name Kamaro (Karlsruhe maize robots) suggests we make maize and robots. While we have been obsessing about the robot part, we have been lacking maize as of late. Also, we were excited about testing our brand-new driving algorithm! So, we set about growing our very own maize field behind building 70.21. The most important part of planting a healthy maize field was preparing our soil to be fluffy and receptive for our maize seeds. We more or less succesfully employed a marsh rotary tiller.

Up until now we used the Nvidia Jetson Nano for machine learning acceleration purposes. Sadly, running our object detection models wasn’t viable on the Jetson Nano despite using its CUDA-capable GPU. Our Jetson Nano only managed a poor 5 spf (seconds per frame) and had both its RAM and VRAM running out of space constantly. In order to remedy this, we looked for stronger hardware.

Following the release of our object detection deep learning code, model and dataset, we are now publishing the code we used for navigating within a crop row at the Field Robot Event. This code has now been in use at Kamaro for several years, and most recently won Task 1 at the competition in 2021. A detailed description of the algorithm is available in the Readme file on Github and in the Proceedings of FRE 2021.

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.