It’s a great day for the PhenoRob research community! Our new partner project “RegisTer” has secured funding from the BMEL for three years, and we are excited about the promising new findings of their work. The interdisciplinary project RegisTer aims to develop automated routines for the characterization and evaluation of sugar beet varieties based on optical/reflective properties of the plants. Sugar beet is an important part of the economic development in rural areas, and the plants have to endure a high tolerance to stress and disease while having a high yield. Thus, modern and better varieties have to be bred. For each newly bred sugar beet variety, an analysis of the sugar beet must be carried out concerning its distinctness from other varieties and its performance and value characteristics over several years and is finally proven by the Bundessortenamt (Federal plant variety office) for approval. The required but time-intensive manual phenotyping work for these tasks could be replaced through the work of “RegisTer,” which aims to implement plant phenotyping pipelines based on Machine Learning techniques using 3D sensors and high-resolution RGB and multispectral images coming from ultralight drones. This brings the potential opportunity to increase the precision and lower the manual work for breeders and the approval process of the Bundessortenamt.