using sensor information to detect lame cows
TRANSCRIPT
Can sensors detect lame cows?
Claudia Kamphuis, Neil Chesterton, Jennie Burke, Jenny Jago
Key points• The challenge of lameness detection
• No direct measure of lameness
• Can existing sensors detect lame cows?
Lameness in the dairy industry• Impacts on welfare, productivity, profitability
– ~$28,000 per year on average NZ farm
• Visual detection is common practice– Challenging for large herds– NZ farmers fail to identify ~75% of lame cows (Fabian, 2012;
Whay et al., 2002)
Lame?
€16,500
Use these sensor data to aid / replace visual detection?
Many sensors available on-farm
Pedometers and EID for activity
and milking order
Weigh scales measuring live-weight
Milk meters measuring
yield aspects
Data • 5 Waikato farms
• 4,900 cows
• 1.5 million milkings
• Sensor data every milking
– transferred automatically each evening to DairyNZ
Lameness events
• Trained Farmers
• Farmer observations– Cow identification– Date– Affected limb– Lameness score– Lameness type
1
2
3
4
5
Lameness events after 1.5 years• 466 lame cows (lameness score ≥2)
Score Type Total
Foot rot Solar damage
White line
Other
2 23 17 24 10 74
3 49 33 43 30 145 (46%)
≥4 28 24 24 8 84
Missing 6 3 3 3 15
Total 106(33%)
77 91 41 318
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
Day
Methods
Matching farm, date
1 lame cow 10 non-lame cows
Day of observation
Lame, n = 318
Non-Lame, n = 3,180
A
ctiv
ity
High
Low
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0320
340
360
380
Day
Change in activity
● Lame (n = 318); ○ Non-lame (n = 3180)
Day of observation
Activity (steps / hour)
Patterns through time were different (P<0.05)
● Lame (n = 318); ○ Non-lame (n = 3180)
Changes in other parameters
-14 -12 -10 -8 -6 -4 -2 045.0
50.0
55.0
60.0
65.0
Day
Milking order
-14 -12 -10 -8 -6 -4 -2 08.48.68.89.09.29.49.69.8
10.0
Day
Milk yield (kg)
-14 -12 -10 -8 -6 -4 -2 0470
475
480
485
490
495
500
Day
Weight (kg)
Patterns through time were different (P<0.05) for all sensors
Changes in milking order - Type
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 045
50
55
60
65
Solar damage Non-Lame cows
Day
Day of observation
Milking order (%)
Changes in milking order - Type
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 045
50
55
60
65Other Solar damageWhite line Non-Lame cows
Day
Day of observation
Milking order (%)
Changes in milking order - Type
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 045
50
55
60
65Foot rot OtherSolar damage White line Non-Lame cows
Day
Day of observation
Patterns through time were different (P<0.05) for milking order and milk yield (2min and total)
Milking order (%)
Changes in activity – Score 2
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0300
320
340
360
380
Non-lame
Score 2
Day
Day of observation
Activity (steps / hour)
Changes in activity – Score 3
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0300
320
340
360
380
Non-lameScore 2Score 3
Day
Activity (steps / hour)
Day of observation
Changes in activity - Scores
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0300
320
340
360
380
Non-lameScore 2Score 3Score ≥4
Day
Activity (steps / hour)
Day of observation
Patterns through time were different (P<0.05) for all except for milk yield (P = 0.15)
Difference in activity La
me
Non-
...
Lam
e
Non-
...
Lam
e
Non-
...
Lam
e
Non-
lam
e
Lam
e
Non-
...
-250
-150
-50
50Farm 1 Farm 2 Farm 3 Farm 4 Farm 5
Difference in Activity between Day -1 and Day -4
Difference in activityLa
me
Non
-Lam
e
Lam
e
Non
-Lam
e
Lam
e
Non
-Lam
e
Lam
e
Non
-lam
e
Lam
e
Non
-Lam
e
-600
-300
0
300
600Farm 1 Farm 2 Farm 3 Farm 4 Farm 5
Difference in Activity between Day -1 and Day -4
No simple threshold to detect lameness
Yes - Sensors can identify changes in behaviour and physiology
ButNo obvious thresholds to separate lame and
non-lame cows
Conclusion
Future researchWhat data are most predictive?
Combining data?
What technique to model noisy data?
Acknowledgements• Jennie Burke, Jenny Jago and Neil Chesterton
• Barbara Dow, DairyNZ
• Participating farmers
• Primary Growth Partnership and DairyNZ Inc.
Data Mining• LogitBoost
– Regression Trees– Boosting
• uni- and multivariable
• Performance measures– AUC– SN at fixed SP
Predictors of Lameness• Live-weight, Activity and Milking order
– AUC = 0.6 – 0.65
• Combined– AUC = 0.75– Significantly better – Consistent across farms