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 [email protected]

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


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 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

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

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 [email protected]

Dairy Farming Scenario


CONTENT


EDERMAIER FARM

Josef Edermeier

~ 140

~ 60

• 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

Lely

• MilkAmount_24

• MilkAmount_7d

• NumberOfMilkings_24h

• NumberOfMilkings_7d

• Rumination_24h

• Weight

AMA

• 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

Lely


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!

[email protected]


WS AGENDA

Technology



Goran Lazendic, Smartbow [email protected]

Technology


CONCEPT

Service Gateway (RRM)

getFoodConsumption

getWeight

getMilkings

Plugin

Plugin

Plugin

SADI Services

HTTP


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

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


SERVICE BESCHREIBUNG


INPUT KLASSE


INPUT KLASSE

Beschreibt RDF Daten, die konsumiert

werden.

Die „Property Restrictions“ definieren welche

Daten benötigt werden.


OUTPUT KLASSE


OUTPUT KLASSE

Beschreibt RDF Daten, die erzeugt werden.

Die „Property Restrictions“ definieren die

Daten, die man erhält.


INPUT/OUTPUT

Example:Individual

Kuh

type

AT123454566

hatLebensnummer

AT123454566

AT123454566

AT123454566

AT123454566AT123454566

hatGeburtsdatum

hatName

hatZugangsdatum

hatVaterLN

hatMutterLN

Input

Output


SAMPLE INPUT


SAMPLE OUTPUT


TOOLS PDE


PDE DATATYPE PROPERTY

SUCHE


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

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


C++

› pluginfactoryinterface.h


C++

› plugininstance.h

Alle virtuellen Methoden überschreiben


C++

› 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