Download - Precision livestock farming cattle identification based on biometric data by Dr. Tarek Gaber
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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
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Overview Introduction Current work (cattle identification) Proposed System Experimental Results Conclusion
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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]
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Introduction: Livestock Farm
Livestock farming in the past
The farmer spends some time for noticing and monitoring
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Livestock Farming Today
Experts do audio-visual scoring by visiting farms and looking to 0behavior of animal.
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Precision Livestock Farming (PLF) “ Management of livestock farming by continuous automated real-time monitoring/controlling/tracing of production/reproduction, health and welfare of livestock.”
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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.
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Research Area in PLF Examples of research points of PLF
Monitoring feed times Feed in-take Condition scoring Real-time analysis of sound
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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
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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.
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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
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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).
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Results
Accuracy results (in %) when applying our proposed algorithm using different training images
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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
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Thanks