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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

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Visions of The Virtual Slaughterhouse

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  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…

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