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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
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
The farmer spends some time
for noticing and monitoring
Livestock Farming Today
Experts do audio-
visual scoring by
visiting farms and
looking to 0behavior
of animal.
Precision Livestock Farming (PLF)
“ Management of livestock farming by continuous automated real-time
monitoring/controlling/tracing of production/reproduction, health and
welfare of livestock.”
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
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).
Results
Accuracy results (in %) when applying our proposed
algorithm using different training images
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
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