Rumen Fill, 3D Computer vision and proactive cow health
Project overview
Participants
More related research
Global goals
- 3. Good health and well-being
- 9. Industry, innovation and infrastructure
- 17. Partnerships for the goals
Short summary
We use 3D cameras and computer vision to assess rumen fill [UE1.1](an indicator of how much cows have eaten) and link its dynamic to subclinical signs of disease. The project is a collaboration between SLU, DeLaval and Högskolan Dalarna.
I. Technical development
Produce tools that are able to segment the paralumbar fossa on 3D images and compute curvature features that best reflect rumen-fill state. Follow curvature dynamically to estimate rumen contractions.
II. Link with health
Look for indicators for subclinical cases of diseases, for example blood markers, as well as recorded treatments. Assess the link between rumen-fill dynamic and health.
III. Proactive monitoring
Deploy automated rumen-fill scoring in the barn and use it to identify cows at risk or that already show signs of compromised health or welfare.