agri openlink final_project_ws

Smartbow GmbH I 4675 Weibern I Jutogasse 3 I AUSTRIA I www.smartbow.at

The final project Workshop Vienna, Austria, 25 Mai , 2016

Dr. Dana Tomic, Smartbow GmbHDana.tomic@smartbow.at

Welcome to the final project workshopData Integration in Precision Farming

from Vision and Needs to Technology and Products

FROM VISION AND NEEDS TO

TECHNOLOGY AND PRODUCTS

FROM VISION AND NEEDS TO

TECHNOLOGY AND PRODUCTS

VISION

Vision

WS AGENDA

Concepts

Applications

Project and Technology

FROM NEEDS TO THE IMPACT

ChallengesNeeds

SolutionTechnology

Project

Outreach Innovation

THE NEEDS

Agricultural Equipment

Sensor-based Monitoring Systems

Decision Support Systems

THE NEEDS

Agricultural Equipment

Sensor-based Monitoring Systems

Decision Support Systems

THE PROJECT

MILK PRICES – THEN AND NOW

THE CHALLENGES

Technology Needs Challenges

Interfaces GUI for visual inspectionProprietary data interfaces

Data Standards Existing but slow Low adoption

Data Integration Time consuming

Knowledge IntegrationDecision Support

Hard-codedOn a single aspect only

Process Adaptiveness

Low / No

System Integration Time consumingDifficult to justify

THE TECHNOLOGY

Challenge agriOpenLink Technology Choice

Data StandardsData Integration

Machine readable descriptionsSemantic models, ontologiesOnline managed - Linked Open (meta) Data

Interfaces Service-based interfacesWeb Service technology (HTTP Rest)Semantic WS (SWS SADI)Service registriesAPI based access to data / services

Process Adaptiveness

Function as a (semantic) service, Processes as adaptive chains of (semantic) services, put together based on needs

Knowledge IntegrationDecision Support

Flexible and extensible knowledge modelsOntologies for classification, rulesQueries on the knowledge graph

System Integration Modular plugin approach - a plugin as a service wrapper with standard interfaces

THE VISION

Integrate knowledge

Provide decision support

Connect any data

Integrate any system

THE VISION

Integrate knowledge

Connect any data

Integrate any system

Provide decision support

Plugin

Semantic Web Services

Ontologies, Registries, Triplestores

Queries, Rules, Logic

Processes – SWS chains

THE SOLUTION

Query Engine and Service

Chaining

Ontology

HTTPPluginServer Plugin Service Discovery

Query Editor

Plugin Development Environment

Plugins

Service Registry

Plugin Service Invocation

On-farm Plugin Gateway

Platform API

Services

Model Editor

Cloud Platform

OUTREACH 2013

OUTREACH 2014

OUTREACH 2015 & 2016

IMPACT - RESEARCH

Data analysis based on a semantic DWH and patternrecognition, for prevention of health issues, e.g. theketosis.

Information Management in Precision Dairy Farming

IMPACT - INNOVATION

http://www.tun-fonds.at/preistraeger/

IMPACT - INNOVATION

Selected as one of the 10 best innovation projects (of 50 initially endorsed) in the 4th phase of the 2nd

call of FINODEX - the FIWARE and open data accelerator.

www.dadafi.io

A platform providing standard interfaces to data, simplifying advising, improving monitoring, accelerating decision making, and stimulating adoption of innovations in precision dairy and livestock farming.

Video: http://bit.ly/1XtVGXY

Thank You

Dr. Dana Kathrin Tomic

-------------------------------------------------------

Big Data, Open Data, Linked Data Services

Smartbow GmbH | www.smartbow.at

MQM 3.2 | A-1030 Vienna | Austria

Tel: +436769129023

More details in the use-case part and in the demo session.

WS AGENDA

Concepts

Applications

Use Case: Dairy Farming

WHY PRECISION DAIRY FARMING?

› Optimized costs of man power, feeding, medication, breeding

› Improved production, fertility of animals, milk quality, herd sustainability

› Improved wellbeing of animals - new standards of wellbeing will come

› Early prevention of health issues

› Avoid human errors but keep humans in the loop

› Possibility to offer new products – diversification

› But technology has to be improved first– Standard interfaces

– Data integration

– Better data access LamenessMastitisKetosis

https://www.youtube.com/watch?v=JbdV4vOhO7A

LOCALIZATION AND HEALTH MONITORING

https://www.facebook.com/smartbowcow

A DIGITAL COW

PDF- Univ. of Kentucky:

http://bit.ly/1Mz4UiI

Die totale ÜberwachungRind - Ausgabe 11/2014Topagrar.com

SENSORS QUO VADIS?

› Rutten et al (2013), Sensors to support health management

› Journal of Dairy Science, Volume 96, Issue 4, Pages 1928–1952

› Based on 126 pubs. /139 systems

› 4 Level Model of functionality

› Topics: mastitis (25%), fertility (33%), locomotion (30%), metabolism (16%)

› For level 1&2 the detection performance, the selection of indicators, sensor techniques, and gold standards require further work

› None of the systems is on Level 3&4 !!

Level 1: Measurement

Level 2 Interpretation

Level 3Integration

Level 4 Automation/DS

PDF QUO VADIS?

› Bewley J (2013) Exciting dairy breakthroughs:

science fiction or precision dairy farming? In 'Proceedings of the

precision dairy conference and expo PDF 2013.

› How to

quantify add-

value for a

PDF system?

› A single

solution is not

sufficient for

effective

advice!

› An integration

concept is

needed !!

DATA INTEGRATION CHALLENGE

MSc Domagoj Drenjanac, Smartbow GmbHDomagoj.drenjanac@smartbow.at

Dairy Farming Scenario

CONTENT

EDERMAIER FARM

Josef Edermeier

• Lely• Wasserbauer• Smartbow

DATA INTEGRATION

CHALLENGE 1

DATA INTEGRATION

CHALLENGE 2

› Compare rumination values from Smartbow and Lely systems

› Inspect how a milk yield relates to the rumination

› Show how the number of milking relates to the rumination

› Focus on anomalies data filtering

SYSTEM ARCHITECTURE

Cloud Platform Plugin Gateway

Plugins

Our Cloud Local on farm

PLUGIN GATEWAY 1

› Hardware

– Intel NUC• Intel i3 processor

• 4 GB RAM

• 60 GB SSD

• Network interface

• Ubuntu OS

• ~ 250 EUR

PLUGIN GATEWAY 2

› Software

– Loads provided plugins

– Utilizes plugins to collect data from heterogeneous devices

– Provides Semantic Web Services (SWS) for data access

– Maintains local registry of SWS

– Maintains local registry of all cows

SYSTEM ARCHITECTURE

Plugins

PLUGINS ON THE FARM 1

› Smartbow, Lely, AMA plugins

› Plugins semantically annotate input data (cows) automatic composition of Semantic Web Services

Smartbow

• Rumination_24h

• HeatAlarm

• MilkAmount_24

• MilkAmount_7d

• NumberOfMilkings_24h

• NumberOfMilkings_7d

• Rumination_24h

• Weight

• GetAllCows

PLUGINS ON THE FARM 2

Plugin Gateway with provided

services

Smartboweartags

(hardware)

AT 192 314 718

Rumination_24h : 500 min

AT 192 314 718AT 192 314 718

AT 192 314 718

Rumination_24h : 500 min

RESTSOAP

DB access

? rumination

SYSTEM ARCHITECTURE

Plugins

CLOUD PLATFORM

› Providing a remote access to farm data

› A server with a plublic IP address

› Data collection, processing, and filtering

› Hosting custom Web Services for data processing expose REST interfaces for client applications

› Dynamic API – dynamic service composition based on SPARQL query language

› Hosting a custom client application Edermaier farm

› Ongoing work: storing data in the cloud

SYSTEM ARCHITECTURE

Plugins

DATA PRESENTATION

› A client application for Edermaier farm

› Requirements from the domain experts

› Responsive web application – Angularjs

– same code for mobile devices

– and browsers

› Deployed on our server with a public IP address remote access to data

COW LIST

COMPARE RUMINATION

Smartbow

MILK YIELD

NUMBER OF MILKINGS

ALARMS

HEAT ALARM

ONGOING WORK

› Ongoing work:

– Testing a push component for plugin gateway

– Testing accumulo big data storage

– Extending the existing dynamic API component

dynamic Web Services

– Extending an existing identity manager

– Extending an existing tool for plugin development

Thank you for attention!

domagoj.drenjanac@smartbow.at

WS AGENDA

Technology

Goran Lazendic, Smartbow GmbHGoran.lazendic@smartbow.at

Technology

CONCEPT

Service Gateway (RRM)

getFoodConsumption

getWeight

getMilkings

Plugin

SADI Services

PLUGIN DEVELOPMENT

1. Service BeschreibungSemantische Beschreibung der Services und zugehörigen Input und

Output Klassen.

2. C++ Implementierung

SERVICE BESCHREIBUNG

› Service Beschreibung hat zwei Teile:

› 1. Beschreibung der Services eines Plugins mit der

http://www.mygrid.org.uk/mygrid-moby-service#

Ontologie

› 2. Semantische Definition der Input und Output Klassen

SERVICE BESCHREIBUNG

INPUT KLASSE

Beschreibt RDF Daten, die konsumiert

werden.

Die „Property Restrictions“ definieren welche

Daten benötigt werden.

OUTPUT KLASSE

Beschreibt RDF Daten, die erzeugt werden.

Die „Property Restrictions“ definieren die

Daten, die man erhält.

INPUT/OUTPUT

Example:Individual

AT123454566

hatLebensnummer

AT123454566

AT123454566AT123454566

hatGeburtsdatum

hatName

hatZugangsdatum

hatVaterLN

hatMutterLN

Output

SAMPLE INPUT

SAMPLE OUTPUT

TOOLS PDE

PDE DATATYPE PROPERTY

C++ IMPLEMENTIERUNG

Platform: Linux

Entwicklungsumgebung: >=QT5

Architektur: x86,armv7 (tested)

QT PROJEKT STARTEN

› https://vsrv2.smartbow.at/goran/plugindevelopment.git

› Projekt in Qt Creator importieren

› Interface Dateien mit eigene Klassen überschreiben

› pluginfactoryinterface.h

› plugininstance.h

Alle virtuellen Methoden überschreiben

› doWork() überschreiben Request von Server

Im Plugin benutzten Properties

Code Ausführung nach definierten Diensten

ZUSAMMENFASSUNG

› Ein Developer erzeugt folgende Dateien

1. Eine dynamische Bibliothek die zur

Laufzeit geladen wird

2. Zwei RDF Dateien: „Service description“

und Beschreibung der Input/Output

Klassen

GELADENES PLUGIN (AMA)

Thank you for attention!

Gooran.lazendicsmartbow.at

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