This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.

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. 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
 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