Aurea Imaging is an AgTech scaleup focusing on precision farming in orchards. By retrofitting our TreeScout sensor package to their tractors, farmers are able to collect data about their orchard down to the tree level as they perform other tasks. Using onboard stereo cameras, the tractor’s high-precision GPS, a machine-vision pipeline running in real time on the device, and cloud-based analytics, this data is turned into maps used by other agricultural machines that enable the grower to utilize less labour and chemical products to produce more food.

To run the edge part of this process, we opted against a traditional embedded/edge architecture based on Robot Operating System (ROS) and C/C++, and instead chose to build Python microservices orchestrated with K3s. This has brought the usual benefits of cloud-native tooling, but building cloud-native software for a far-edge, occasionally-airgapped application in an ecosystem based on decades-old standards comes with a myriad of challenges not faced in traditional cloud environments. Overall, Kubernetes on edge has brought us a very high development velocity and a reliable, maintainable codebase, and we hope to both explore the challenges this brings as well as inspire others to try this approach for their next edge project. On top of this we are running object detection models in tensorRT which are able to detect tree specifics at a driving speed of 8km/hr.