1 / 13

Visions of The Virtual Slaughterhouse

Visions of The Virtual Slaughterhouse. Søren G. Erbou Ph.D. student Informatics and Mathematical Modelling Technical University of Denmark Danish Meat Research Institute sge@imm.dtu.dk. Outline. What is the The Virtual Slaughterhouse? Where are we now? Visions for the future Means

tobias
Télécharger la présentation

Visions of The Virtual Slaughterhouse

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Visions of The Virtual Slaughterhouse Søren G. Erbou Ph.D. student Informatics and Mathematical Modelling Technical University of Denmark Danish Meat Research Institute sge@imm.dtu.dk

  2. Outline • What is the The Virtual Slaughterhouse? • Where are we now? • Visions for the future • Means • Applications • Summary

  3. The Virtual Slaughterhouse • Part of the Danish Meat Research Institute (Roskilde, 1954) • Owned by the Danish pig producers via the Danish Meat Association • Mission of DMRI • Leading knowledge centre within meat and slaughter technology • The Virtual Slaughterhouse (2006-08) • 3D-models of pig carcasses and tools for analysing the models • Collaboration with IMM, University of Århus and Visiana • Own CT-scanner • 4 phd-students • Why?

  4. Where are we now? • Pig slaughter automation programme (1998->) • >30 development projects, >40 mill. € • Improved working environment, hygiene and safety • Examples • Cleaning of throat- • and heart regions • Removal of inner • bones from front part

  5. Where are we now? • Highly automated production • Need for better quality control • Reverse problem in the production • Normal: Many small parts assembled into one final product • Reverse: Same input ”disassembled” into many products • Large variability of input • Products restrict each other • Minimise variability on output and maximise profit • New methods for production planning • Operations research • Define new input features

  6. Visions for the future • Where do we want to be in 10 years? • More adaptable production • Better suited for niche production • Less variability in quality of the final products • Handle large variability of input • Keep the leading technological position worldwide

  7. Means • Modelling the biological variability • Modelling specific cuttings • New predictors of quality based on models • Optimise the cutting of each carcass separately • Introducing the CT-technology to the slaughterhouse industry • Image analysis is the key for extracting only the necessary information

  8. Online CT • Adapt cutting to each specific carcass • Unlimited amount of information available at an early stage along the slaughterline • Extract useful information • Define predictors for quality • Speed and cost is crucial • Minimise data acquisition while maximising useful information

  9. Applications • Trimming of the fat layer on the loin or neck muscle • The same fat thickness all over the loin gives the best price • Small thickness is better • Important not to expose the muscle • CT can give additional information • Model the fat layer • Fit the model to new carcasses (University of Nebraska, Lincoln)

  10. Applications • Use models for developing robotic tools • 40 carcasses CT-scanned • 3D statistical shape model of bone structures in the ham. • 7 model parameters describe 69% of the variation • Parameters are localised to ease interpretation

  11. Even more applications… • Segmentation of muscles • Cutting and quality estimation of pig backs • Pig atlas • Volume registration • Apply cuts in atlas and propagate to population • Virtual dissection • Voxelwise classification • Better quality measure • Many more to come…

  12. Summary • Obtaining and analysing 3D-models of pig carcasses • Introducing CT in the slaughterhouses • Image analysis and statistics are key elements • Predictors for quality • From models to robotic tools • Better suited for niche products • Production more adaptable • Quality control is crucial

  13. Aknowledgements DMRI: Eli V. Olsen Lars B. Christensen Claus Borggård IMM-DTU: Martin Vester-Christensen Mads F. Hansen Peter S. Jørgensen Allan Lyckegaard Rasmus Larsen Bjarne K. Ersbøll, Thank you for your attention…

More Related