semantic web enabled smart farming

Semantic Web Enabled Smart FarmingSemantic Machine Learning and Linked Open Data Application for Agricultural and Environmental Informatics

CSIRO COMPUTATIONAL INFORMATICS

Raj Gaire | Research Software Engineer

22 October 2013

IN COLLABORATION WITH

Smart Farm

• Informed Farming• Precision agriculture

– Sensors, information system, decision support systems

– System exists within a farm-gate

• Connected Farm• Devices in the farm are connected with each other and the world using

internet

• Farmers are connected to the farm devices, other farmers and experts

• Things (e.g. Cattle) in the farm can be monitored remotely.

• Integrated Farm• Includes Farmers in the supply chain - suppliers, logistics, consumers – back

to the farmers to complete the loop.

Presentation title | Presenter name2 |

Kirby ‘Smart’ Farm

• Location Armidale, NSW, Australia

• Farm Area: 739 Hectares (or 1827 Acres)

• Smartfarm Area: 269 Hectares (or 665 Acres)

• Livestock: Cattle, Sheep

• Devices: 100 Soil Sensors

2 Weather Stations

Cattle ear tags

Flex, Alix PC, 3G Modem etc.


What do farmers want?

• Measurement data produced by 100 sensor every couple of minutes?

• Weather measurement produced every couple of minutes?

• Cattle location updated frequently?

• Farmers are interested in the alerts about the things in the farm.• Cattle leave the farm

• When to sow

• Current market value of their livestock

• Soil in a paddock is compacted

• Researchers/Experts are interested in the data.


Our Architecture


Smartfarm Ontology


Data Dimensions


GSN Extended

• Geo-Spatial Analysis• Implemented using R and Java packages

• Event (Alert) Processing• Extended GSN to process event descriptions and produce alerts

• Synchronous and Asynchronous events

• Farms can create their own events

• Semantic Web Enablement• Sensor data stored in MySQL

• Linked data are produced using defined URIs

• Statistical data are stored in Virtuoso triple store

– Provides open access to everyone, analyse data using SPARQL

– VisualBox and Google APIs for visualisation


Event Detection


Web Form… …. …. ….… …. …. ….… . Submit

Event Manager

Event Description

Storage

Event Evaluator

Event VirtualSensor

Message Queue

GSN Storage

Event Description

Alerts

Important Links

PURPOSE LINK

Homepage (ROOT) http://smartfarm-ict.it.csiro.au

Semantics http://smartfarm-ict.it.csiro.au/semantics.jsp

Latest Data http://smartfarm-ict.it.csiro.au/latest

Specific Latest Data ROOT/dataset/sensornets/kirby-farm/type/{id} [/latest

Time Series Data Cube ROOT/dataset/sensornets/kirby-farm/{type}/{id} [/year/{year}/[month/{month}/[day/{day}/[hour/{hour}]]]]

VisualBox Home http://kirbyfarm-virtuoso.dyn.dhs.org/visualization/

SPARQL endpoint http://kirbyfarm-virtuoso.dyn.dhs.org:8890/sparql


http://smartfarm-ict.it.csiro.au/

http://smartfarm-ict.it.csiro.au/semantics.jsp

http://smartfarm-ict.it.csiro.au/latest

http://kirbyfarm-virtuoso.dyn.dhs.org/visualization/

http://kirbyfarm-virtuoso.dyn.dhs.org:8890/sparql

Visualisation


Future Works

• SPARQL based access to dynamically generated data cubes

• Machine Learning over the Data

• Integrate satellite data

• Social Farming


Machine Learning Opportunities

• Cost of Sensor Networks

• Variations are possibly correlated and predictable• Soil variation, elevation -> soil ec, temp, vwc

• BOM forecast -> farm weather

• Data collected over last 2 years • Use to generate predictive model

• Produce sensor data without sensors.


Because data from Sensor networks in farms worth more than the sensor networks!

Acknowledgement

Kerry Taylor

Laurent Lefort

Michael Compton

David Henry

Ali Salehi

David Lamb

Gregory Falzon

Derek Schneider

Ashley Saint


Computational InformaticsRaj GaireResearch Software Engineer

t +61 2 6216 7090e [email protected] www.csiro.au/CCI

CSIRO COMPUTATIONAL INFORMATICS

Thank you

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