Automated Mastitis Detection

for Dairy Farms

Amanda Sterrett & Jeffrey BewleyUniversity of Kentucky

Dairy Systems Management

But how we monitor it is different…

Why do these people

keep hanging stuff off

of me?

What else can we monitor?

Because cows are routine-oriented, we can monitor their behavior and examine differences in:

Eating time / DMI

Standing / Lying time

Rumination time

Location within barn

Take advantage of simplicity

Body temperature

Ear, milk, reticulorumen, udder, vagina

Milk composition

SCC

Fat, lactose, protein, LDH, etc.

Electrical conductivity

Physiological monitoring

Potential Benefits

Early Mastitis

Detection

Early Treatment

Improved Treatment Outcome

Less Economic

Loss

Improved Prevention

Program

Less Production

Loss

Improved Animal Well-

Being

Accuracy and Precision

Sensitivity and Specificity

Sensitivity (true positive rate): alert with an observed mastitis case

Specificity (true negative rate): no alert with no mastitis

In-Line Monitoring

Ion concentration of milk changes during mastitis

Inexpensive and simple equipment

Wide range of sensitivity and specificity reported

Affected by sample time, milk viscosity, temperature, and sensor calibration

Most useful when combined with other data

Electrical Conductivity

Automated CMT or WMT

• CellSense (New Zealand)

• r = 0.76 with Fossomatic SCC

Alert based on EC

Alert based on In-Line SCC

Alert based on EC and SCC

Alert time perio

d

Observ-

ation period

Sensitivity

False

alert rate

Sensitivity

False alert rate

Sensitivity

False

alert rate

96 48 80 4.7 83.3 2.9 80 1.2

48 24 80 7.8 83.3 3.7 80 2.1

Somatic Cell Count

• In-line detection of cell count, milk temperature, and electrical conductivity

• Uses ATP luminescence as an indicator of the number of somatic cells

• Sensor connected to the milk hose below the milking claw

• Reagent cassette attached below display

Spectroscopy

• Visible, near-infrared, mid-infrared, or radio frequency

• Indirect identification through changes in milk composition

• AfiLab uses near infrared

– Fat, protein, lactose, SCC, and MUN

LDH Threshold (µmol min-1|-1)

Sensitivity

Specificity

4.3 95.2 92.06.5 72.6 98.5

http://blog.modernmechanix.com/robot-cow-moos-and-gives-milk/

Cow

Sensors

Temperature Limitations• Not all cases of mastitis result in a

temperature response

• Best location to collect temperature?

• Noise from other physiological impacts

Udder Thermography• Udder temperature closely related to rectal

temperature

• No early detection in LPS challenge (Hovinen et al., 2008)

• Potential use in dry cows

Hovinen et al., 2008

Before Infection After Infection

Accelerometers

• Measures lying time and activity/motion index

• Well researched and applied to many areas

• Lying is a high priority behavior

• May change lying time around mastitis

• May decrease activity around mastitis

• Lying time decreased by 73 minutes on the day of challenge (P < 0.01, Cyples et al., 2012)

Rumination Behavior

• Cows with mastitis may ruminate less

• r = 0.93 for automated rumination with live observations in cows (Schirmann et al., 2009)

Animal Position

• Real Time Location System

• Cows may stay in same spot longer around mastitis

Multi-parameter Sensors• Combination monitors may find a better

market than those sensors only targeted at one parameter:

– Temperature

– Activity

– Rumination

– Feeding Time

• Multivariate analyses

Economics

Positive return on investment

Producer satisfaction

What data is useful?

Reading frequency

What do we do with the data?

Culture, monitor, treat, ignore?

Considerations

Using technologies for mastitis monitoring is newer than using them for estrus detection

Algorithms are not yet perfected

Continued research is needed, particularly in naturally occurring mastitis

Conclusions

Questions?

Amanda Sterrett408 WP Garrigus BuildingLexington, KY 40546412-558-2075amanda.sterrett@uky.edu

Dr. Jeffrey Bewley407 WP Garrigus BuildingLexington, KY 40546859-699-2998jbewley@uky.edu