Traditionally, plant phenotyping—the science of accurately recording plant characteristics—has relied on time-consuming, manual measurements. Today, these processes are increasingly automated, supported by advanced sensor technologies and machine learning. These technologies record parameters such as size, fruit quality, leaf shape, and growth rates. Automated systems can often gather complex information about a plant that is difficult for humans to determine on a large scale.

A key aspect of this sensor-based breeding is the availability of precise reference materials. The sensors require data on a “standard plant” that includes all relevant characteristics, including three-dimensional properties such as leaf angle. A physical model offers clear advantages over purely digital or two-dimensional representations. For example, it can be used as a reference and internal control instance in a greenhouse or test field under real plants.

With these applications in mind, researchers have developed a new 3D-printed model of a sugar beet plant. The print files for this model are freely accessible, enabling researchers worldwide to create exact copies of the reference model. This promotes the comparability of research results and facilitates the application of the method even in resource-poor environments, such as developing countries.

Jonas Bömer and colleagues from the Institute for Sugar Beet Research (Göttingen) and the University of Bonn used LIDAR (Light Detection and Ranging) technology to collect the precise data for the realistic model. A real sugar beet plant was scanned with a laser from twelve different angles. After processing the data, the model was created using a commercial 3D printer and tested for use in both the lab and field.

This innovative approach in plant phenotyping is set to revolutionize the field by providing accurate, reproducible reference models that can enhance research outcomes and support global agricultural advancements.

By Impact Lab