Adaptive Agricultural Processes

via Open Interfaces and Linked

Services

Dr. Dana Tomic

FTW Forschungszentrum Telekommunikation

Wien, Austria

IKT der Zukunft (1. Ausschreibung 2012)

Budget: ~800 k Euro

Laufzeit: 06/2013 - 05/2016 (36 Mo)

Anwendungsfeld: Produktionssysteme

Themenfelder:

• Datendurchdringen

• Semantische Technologien

• Schnittstellen von Systemen

Advanced Technology

• ICT, Sensors, robots, GPS, Decision Support Systems, Reporting, Tracking, Tracing

• Showcase for the Internet of (or with) Things

• Plug-and-play

Benefits

• Cost savings, quality improvement

• High precision of application, impact reduction, sustainability

• Process optimization

From Data to Knowledge

• Data integration

• Knowledge management

• Add-value services

API

+

tool

box

Semantic

Services

Interface Data Models

Ontologies

(Domain,

Services,

Sensors,

Interfaces)

Process

Model for

Optimization

Semantic

Service

Composition

Information and Advisory System

Hardware Platforms

Workflow

Management

- Vocabularies = concepts and relationships (also referred to as “terms”)

used to describe and represent an area of concern.

- classify the terms

- characterize possible relationships

- define possible constraints on using those terms.

- can be very complex (with several thousands of terms) or very simple

(describing one or two concepts only).

- Ontology = explicit formal specifications of the terms in the domain and

relations among them

- Classes, Object Properties, Data Properties, Instances

- Reasoning = Classification, creation of new facts

- Description techniques:

- RDF and RDF Schemas

- Simple Knowledge Organization System (SKOS)

- Web Ontology Language (OWL)

- Rule Interchange Format (RIF).

FAO - Food and Agriculture Organization of the United Nations (FAO;

http://aims.fao.org) - is developing agriculture information

management standards such as AGROVOC thesaurus, Agris and

openAgris.

AGROVOC:

- a controlled vocabulary covering all areas of interest to FAO, including

food, nutrition, agriculture, fisheries, forestry, environment etc.

- formalized as a RDF/SKOS-XL linked dataset

- accessible through a SPARQL endpoint

- Available as open linked data, used for labeling of AGRIS data

Other thesauri and ontologies ( USDA, CSRO, MUNI ontology)

To share common understanding of the structure of information

among people or software agents

To enable reuse of domain knowledge

To make domain assumptions explicit

To separate domain knowledge from the operational knowledge

To analyze domain knowledge

To share common understanding of the structure of information

among people or software agents

To enable reuse of domain knowledge

To make domain assumptions explicit

To separate domain knowledge from the operational knowledge

To analyze domain knowledge

To share common understanding of the structure of information

among people or software agents

To enable reuse of domain knowledge

To analyze domain knowledge

To make domain assumptions explicit

To separate domain knowledge from the operational knowledge

To have benefit of automatic reasoning

Collect Data in

Repository

Trigger Reasoner

Ontology

Rules

Trigger Actions

based on Results of

Reasoning

Maintain domain

knowledgeData

Actions

Determine the domain and scope of the ontology

- Use Cases: 1) Diary Farming 2) Precision Farming

- System Ontology

- Service Ontology

Consider reusing existing ontologies

- Agriculture domain, upper ontologies, sensor ontologies

Enumerate important terms in the ontology

- Farm, Animal, Milk, Food, Equipment, Users, Services, Process, …

Identify relationships

Translate into classes & properties

- Specify primitive classes

- Specify defined classes (for classification based on reasoning)

Define individuals

Work Structure, Timeline, Main Results

OntologiesPlugins

(Plugin

Services)

Platform & System

Services

Developer Tools

Use Cases

Test

Application

User Study

Use-Case Services

Plugins for agricultural

equipment

Core Decision Plugins/sWS

User Interaction Plugins/sWS

System Detailed Architecture

Ontology and Process Management System (OPMS)

(distributed: farm computer & remote platform(s) )

Web Server

Web Server

Service &

Process

Registry

Data

Reposit.

Ontologies

Service & Process

Description Repository

Q&R (SPARQL &

policy-based reasoning)

Web Plugin

Server

Web Server

CD

W

S

UI

W

S

in detail

Web Interface

AnalyticsEq. Plugin S.Eq. Plugin S.UI Plugin S.UI Plugin S.

Core Plugin S. Core Plugin S.

Q&R (SPARQL &

policy-based reasoning)

Execution

Engine

Service

Quality Mng

Composition

Engine

Ontology &

Rule Mng.Process

Quality Mng.

Asset Config.

Mng.

Publish and Subscribe

Domain Modelling

- Detailed modelling of process in selected use cases

- The roles of stakeholders in the process: farmer, veterinarian, milk

company, quality assurance organization, animal tracing organization,

farmer associations

- Selection of ontologies, ontology development

- Extensibility by design

Current Implementation

- Plug-in API development

- Sematic REST services (SADI approach)

- Service execution environment

Next Steps

- Workflow modelling and matchmaking component

- Monitoring and service selection framework

- Recommendation framework

Dr. Slobodanka Dana Kathrin TomicSenior Researcher | FTW | www.ftw.atForschungszentrum Telekommunikation Wien GmbHDonau-City-Straße 1/3 | A-1220 Vienna | Austria+43/1/5052830 -54 | fax -99 | +43/6769129023