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Collaborative project: The use of artificial intelligence and optical sensors for the assessment of characteristics in variety testing in sugar beet - subproject B (RegisTer)
Project
Project code: 28DK108B20
Contract period: 03.03.2021
- 02.03.2024
Budget: 360,948 Euro
Purpose of research: Applied research
Keywords: knowledge transfer, networking, sugar beet, sensor technology, digital world, start-up, AI Artificial Intelligence, crop production, data
Sugar beet is an essential and efficient crop for divers crop rotations. It also represents an important economic factor for rural areas. Modern varieties must have diverse characteristics such as disease and stress tolerance and high yield potential. These characteristics must be recognized quickly and reliably in the breeding and approval process. Also, each variety must be clearly described and be distinguishable. While breeders are developing and testing more and more breeding lines, the Federal Plant Variety Office (Bundessortenamt) has to determine in the approval process whether the breeding result is a new variety and whether this variety also has a value in terms of regional culture. For the necessary description and evaluation of the varieties, phenotypic characteristics have to be collected with great effort in (visual) manual field audits. The goal of the interdisciplinary joint project RegisTer is to develop automated routines for the characterization and evaluation of sugar beet varieties based on the geometric and optical/reflective properties of the plants, which are measured using ultra-light flying drones. Drones, equipped with high-resolution RGB and multi-spectral cameras and 3D sensors are used to measure test fields. A millimeter-precise data set is the basis to automatically analyze the plant parameters with state-of-the-art image processing based on machine learning down to the individual plant level. The aim is to automatically extract (new) plant characteristics and their valuable properties for variety description and variety evaluation. The aim of this project is to achieve automation, standardization, and improvement of the assessment process for the register and value tests at the Federal Plant Variety Office and performance tests in the plant breeding industry, which is characterized by small and medium-sized enterprises.
Section overview
Subjects
- Crop Production
- Computer science
Framework programme
Funding programme
Excutive institution
Funding institution
Project management agencies
Associated projects: RegisTer
- Collaborative project: The use of artificial intelligence and optical sensors for the assessment of characteristics in variety testing in sugar beet - subproject A
- Collaborative project: The use of artificial intelligence and optical sensors for the assessment of characteristics in variety testing in sugar beet - subproject C
- Collaborative project: The use of artificial intelligence and optical sensors for the assessment of characteristics in variety testing in sugar beet - subproject D
