almtech overview - unece€¦ · almtech overview graham gardner this project is supported by...

ALMTech Overview

Graham Gardner

This project is supported by funding from the Australian Government Department of Agriculture and WaterResources as part of its Rural R&D for Profit programme in partnership with Research and DevelopmentCorporations, Commercial Companies, State Departments and Universities.

Outline

• ALMTech overview

• DEXA update

calibration/repeatability

auditing

cut wt prediction

eating quality prediction

ALMTechAdvanced Livestock Measurement

Technologies project

Predict quality and amount of final product

Conception Live Animal Carcase Retail Cuts Cooked Product

ValueValueValueValue

Deliverables!

Predict quality and amount of final product


Hyperspectral

Synergy with Automation


LMY:

Eating Quality:3D

Im

agin

g

DEXA3D Imaging

MIJ camera

Deliverables!

Deliverables!•Predict quality and amount of final product


Synergy with Automation


LMY:

Eating Quality:

Hyperspectral NIR

DEXA 3D Imaging3D Imaging

Commercialise 1 automated offal sorting tech

Commercialise DEXA(5 lamb, 1 beef abattoirs)

Commercialise Porkscan Mk2(2 pork abattoirs)

Commercialise cameras(2 Beef, 2 Lamb abattoirs)

Commercialise 3D imaging(2 beef feedlots)

Standardised Calibration Systems

ICWG(industry calibration working group)

•Predict quality and amount of final product



EnhancedBeef MSA

Cuts-basedLamb MSA

NewBreeding

Values

EnhancedProducerFeedback

Systems toimprove

compliance

Cut wtprediction

systems

Profit functionsto optimisecarcase use0

0.02

0.04

0.06

0.08

0.1

0.12

10 15 20 25 30 35

$

Benchmarkingsystems

0

0.02

0.04

0.06

0.08

0.1

0.12

10 15 20 25 30 35

Deploy algorithmsto enable

value based pricing(2 abattoirs)

Data to geneticand industry

databases(10 supply chains)

Commercialise3 new EBVs Support roll-out of

lamb MSA Mk2(2 supply chains)

Deliverables!

Timeline – LMY Devices

Lamb

Beef

50%

100%

100%0%

0%

Liv

e

Beef

Lamb

Beef

Lamb

Lamb

Pork

Pork

Scan

0%

0%

50% 100%

100%50%

Ca

rca

se

DE

XA

3D

Im

agin

g

Beef

0% 50% 100%

0% 50% 100%

DE

XA

3D

Im

agin

gM

icro

wave

Lamb

0% 50% 100%

Beef

Beef

E+

VB

CC

-3Lamb

Beef

Mic

row

ave

2D X-Ray for driving robots

Nucleus Flock/DEXA

0

5

10

15

20

25

30

35

40

45

10 15 20 25 30 35 40

GR

Tis

sue

De

pth

(m

m)

Hot Standard Carcase Weight (kg)

Nucleus Flock Kill 6 (30/9)






R2=0.92, RMSE=1.31

DEXA predicting CT Fat%

Gardner et al., 2018. Meat Science.

Lamb DEXARepeatability

Immediate Repeatability

Scan Run - Experiment 1

1 2 3 4 5

23.2a 23.4a 23.2a 23.5a 23.4a

Mean Fat % predictions by scan runDiffering superscript denotes significant difference (P<0.01)

Scan carcases 5 times in a row

Connaughton et al., 2019.

CalibrationAdjust for synthetic phantom & air

0

5

10

15

20

25

30

35

0 5 10 15 20

Fat

% P

red

icti

on

Fat % Prediction of Wednesday Group

Data Before Adjustment

10

12

14

16

18

20

22

24

26

28

30

10 15 20 25 30

Fat

% P

red

icti

on

Fat % Prediction of Wednesday Group

Data After Adjustment

Wednesday Group

Thursday Group

Friday Group

Repeatability between days


30

40

50

60

70

80

90

30 40 50 60 70 80 90

Bro

okl

yn P

red

icti

on

(%

Le

an)

Bordertown Prediction (% Lean)

Lean % Prediction with no calibration

Repeatability between sites


30

40

50

60

70

80

90

30 40 50 60 70 80 90

Bro

okl

yn P

red

icti

on

(%

Le

an)

Bordertown Prediction (% Lean)

Lean % Prediction After Phantom Calibration

Repeatability between sites


Auditing

DEXA

Validation/AuditingAcquire imagesFull system

CalibrationAdjust forSyntheticPhantom

Threshold bone Convert to Tissue% Convert to CT lean, fat, bone

Sets air attenuation at 4095

CalibratedImage

High energyLow energy

ln(ILow/AirAtten)

ln(IHigh/AirAtten) R =

Calc. R values

Wt’d pixel depth

Company Specific Algorithm

Clearfield

AirAtten

CalibratedImage





CalibratedImage


ln(ILow/AirAtten)


Calc. R values

Wt’d pixel depth


Clearfield

AirAtten

CalibratedImage

Synthetic Phantom

Auditor Calibration point

Calibration point





CalibratedImage


ln(ILow/AirAtten)


Calc. R values

Wt’d pixel depth


Clearfield

AirAtten

CalibratedImage

Synthetic Phantom

Auditor

Calibration point

Routine auditingensures calibrated image





CalibratedImage


ln(ILow/AirAtten)


Calc. R values

Wt’d pixel depth


Clearfield

AirAtten

CalibratedImage

Calibration point

CT validation required when:1. New DEXA hardware

2. Altered company algorithm3. Disputes

Traits manual

Estimating cut weight using DEXA

Predicting round & shortloin wtusing HCWT plus DEXAfat value

RMSE = 0.054kg

RMSE = 0.105kg

DEXA

Carcase Calculator rework

CT Scanning

DEXA Lean Value = 52



Wt & DEXA & EQ

Optimise carcase usage

Bone out cost

Cut Retail Values

Cut wt by retail value

Adj for cut market volume

Optimised profitCarcase descisions to

optimise profit

$ $ $$$

Characterisecarcase grades

29

Is DEXA relevant to MSA?

Lumbar DEXA R STDEV

Lean Meat Yield%

DEXA predicts Overall Liking

10.3 eating

quality units

8.3 eating

quality units

80 lambs 200-300 days old

Anderson et al., 2019. ICOMST

Lumbar DEXA R STDEV

Lean Meat Yield%

DEXA predicts Overall Liking

10.3 eating

quality units

8.3 eating

quality units

80 lambs 200-300 days old

Independent of LMY%IMF%

Anderson et al., 2019. ICOMST

32

Meat Standards Australia eating quality model


Carcase Wt

IMF

Rib Fat

Maturity

33

Meat Standards Australia eating quality model


Summary

• ALMTech – 3 more years to

accelerate measurement tech

• DEXA calibrated against CT

• Industry system for auditing

• Predicts cut wt & EQ

almtech overview - unece€¦ · almtech overview graham gardner this project is supported by...

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