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

Scientific Research Group in Egyptwww.egyptscience.net

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