The artificial intelligence project “RegisTer,” funded by the German Federal Ministry of Food and Agriculture (BMEL) is on track!
RegisTer is coordinated by Pheno-Inspect GmbH. Together with the Institut Für Zuckerrübenforschung, The University of Bonn, and the Bundessortenamt, we look back on a successful 2021 season. We collected a lot of data and gained many novel insights.
Thanks to Anne-Katrin Mahlein, Cyrill Stachniss, Richard Manthey, Stefan Paulus, Elias Marks, Jonas Böhmer, and your teammates for all the hard work so far!
The interdisciplinary project RegisTer aims to develop automated routines for the characterization and evaluation of sugar beet varieties based on the optical/reflective properties of the plants. We look for features describing the phenotype of a sugar beet varieties to distinguish and register them properly. We also investigate an automated way to score plant diseases such as Cercospora leaf spot, mildew, rust (see GIF below). RegisTer 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.
RegisTer is partially supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany. The Bundesanstalt für Landwirtschaft und Ernährung / Federal Office of Agriculture and Food in Germany (BLE) provides coordinating support for digitalization in agriculture as funding organization, grant number FZK 28DK108A20.