Precision Livestock Farming: Cattle Identification based on Biometric Data By Tarek Mahmmed Gaber, PhD Faculty of Computers and Informatics Suez Canal University Scientific Research Group in Egypt Overview Introduction Current work (cattle identification) Proposed System Experimental Results Conclusion Introduction: What is the Problem Worldwide demand for meat is expected to increase with >40% in next 15 years Health: Relationship between animal health and healthy food Animal welfare Economic importance Others ……. Source: [TIVO-project] Introduction: Livestock Farm Livestock farming in the past Livestock Farming Today Experts do audio-visual scoring by visiting farms and looking to 0behavior of animal. Precision Livestock Farming (PLF) Benefit for Farmers from PLF By automating the farming process, the farmer is able to receive real-time information on his livestock, so can: Manage and optimise animal production and welfare in a fast and accurate way. Research Area in PLF Examples of research points of PLF Monitoring feed times Feed in-take Condition scoring Real-time analysis of sound Animal Tracing: Animal Identification Radio Frequency Identification (RFID) is currently the most well used method for animal identification. Ear tag or as a microchip the skin. Problems: Invasive and religious matters Animal Biometric-based Solutions Can produce accurate results of cattle recognition in real production conditions. Do not need to attach any additional elements with or within the animals. Comply with most countries legal rules (e.g. the current EU legislation) for beef traceability in slaughterhouses. Unique Features of Cattle Breeds muzzle pattern or nose print has been investigated and proven to be unique for each cattle It is then concluded that muzzle print is similar to the human's fingerprint Precision Livestock Farming: Cattle Identification based on Biometric Data Training phase Collecting all training muzzle print images. Extracting the features Representing each image by one feature vector. Applying a dimensionality reduction (e.g, LDA) to reduce the number features in the vector Results Conclusion Precision Livestock farming could Increase the efficiency and sustainability for farming and livestock production by monitoring (individual) animals Our proposal approach for cattle identification could Deliver quantitative information and complete traceability of livestock in the food chain. Image-based identification could be a promising non-intrusive method for cattle identification Thanks

1. PRECISION LIVESTOCK FARMING: CATTLE
IDENTIFICATION BASED ON BIOMETRIC DATA
By
Tarek Mahmmed Gaber, PhD
Faculty of Computers and Informatics
Suez Canal University
08/04/2014 – Faculty of Agriculture, Ismailia, Egypt

2. Scientific Research Group in Egypt
www.egyptscience.net

3. Overview
 Introduction
 Current work (cattle identification)
 Proposed System
 Experimental Results
 Conclusion

4. Introduction: What is the Problem
 Worldwide demand for meat is expected to increase
with >40% in next 15 years
 Health: Relationship between animal health and
healthy food
 Animal welfare
 Economic importance
 Others …….
 Source: [TIVO-project]

5. Introduction: Livestock Farm
Livestock farming in the
past
The farmer spends some time for
noticing and monitoring

6. Livestock Farming Today
Experts do audio-visual
scoring by
visiting farms and
looking to 0behavior
of animal.

7. Precision Livestock Farming
(PLF)
“ Management of livestock farming by continuous automated real-time
monitoring/controlling/tracing of production/reproduction, health and
welfare of livestock.”

8. Benefit for Farmers from PLF
 By automating the farming process, the farmer
is able to receive real-time information on his
livestock, so can:
 Manage and optimise animal production and
welfare in a fast and accurate way.

9. Research Area in PLF
 Examples of research points of PLF
 Monitoring feed times
 Feed in-take
 Condition scoring
 Real-time analysis of sound

10. Animal Tracing: Animal Identification
 Radio Frequency Identification
(RFID) is currently the most well
used method for animal
identification.
 Ear tag or as a microchip the skin.
 Problems:
 Invasive and religious matters

11. Animal Biometric-based
Solutions
 Can produce accurate results of cattle
recognition in real production conditions.
 Do not need to attach any additional elements
with or within the animals.
 Comply with most countries legal rules (e.g.
the current EU legislation) for beef traceability
in slaughterhouses.

12. Unique Features of Cattle
 Breeds muzzle pattern or
nose print has been
investigated and proven to
be unique for each cattle
 It is then concluded that
muzzle print is similar to
the human's fingerprint

13. Identification based on Biometric
Data
 Training phase
 Collecting all training
muzzle print images.
 Extracting the features
 Representing each image
by one feature vector.
 Applying a dimensionality
reduction (e.g, LDA) to
reduce the number
features in the vector
 Testing phase
 Collecting the muzzle print
image,
 Extract the features
 Feature vector is projected
on LDA space.
 Applying machine learning
techniques for classifying
the test feature vector to
decide whether the animal
is identified or not).

15. Results
Accuracy results (in %) when applying our proposed
algorithm using different training images

16. Conclusion
 Precision Livestock farming could
 Increase the efficiency and sustainability for farming
and livestock production by monitoring (individual)
animals
 Our proposal approach for cattle identification
could
 Deliver quantitative information and complete
traceability of livestock in the food chain.
 Image-based identification could be a promising non-intrusive
method for cattle identification

17. Thanks