Title: DiGiSOW
Funded by: GUDP
AU project manager: Tenure Track assistant professor Grum Gebreyesus
Participants:
Tenure Track assistant professor Grum Gebreyesus (PI, QGG)
Tenure Track assistant professor Alban Bouquet (participant, QGG)
Senior consultant Claus Hansen (Project manager, Danish Technological Institute), Food and Production
+45 72 20 24 63
Danish Technological Institute
Gregersensvej, DK-2630 Taastrup
www.dti.dk
Head of Genetics Søren Balder Bendtsen (Danish Pig Genetics)
Direct: +45 8140 2613
Office: +45 8140 2610
Lysholt Allé 10, 2. sal.
7100 Vejle - Denmark
www.danishgenetics.dk
Collaboration partners:
Project period: 10/2024 – 10/2028
Funding amount: 9,996,210 DKK
Project description
In modern livestock breeding, the integration of precision livestock farming (PLF) and computer vision technologies is transforming phenotype development, allowing more accurate and scalable monitoring of traits such as gait, lameness, and leg strength. State-of-the-art high-throughput phenotyping tools, sensor-based platforms, and artificial intelligence (AI) automate the analysis of behavioral and morphological traits in real time. Leveraging machine learning and computer vision frameworks, these technologies generate vast amounts of data that can be integrated into genetic models to enhance breeding programs.
The DiGiSOW project focuses on defining strength and lameness traits in sows, utilizing advanced computer vision technologies to monitor gait and conformation traits. This project aims to deliver new breeding solutions by developing automated phenotyping methods that assess animal behavior and health with high precision. By integrating AI and machine learning into this phenotyping process, DiGiSOW will generate actionable insights into sow robustness and longevity, improving the genetic selection process.
