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 Egypt www.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