This document discusses a "smart farm" project that uses sensors, internet connectivity, and semantic technologies to monitor and manage a farm. The project collects data from 100 soil sensors and 2 weather stations on the farm. It uses an ontology and linked open data to semantically integrate and provide open access to the sensor data. Machine learning algorithms could potentially generate predictive models from the sensor data to estimate values without physical sensors. The system detects events on the farm like cattle leaving and alerts farmers. It aims to help farmers make informed decisions and remotely monitor their farm operations.

1. Semantic Web Enabled Smart Farming
Semantic Machine Learning and Linked Open Data Application for Agricultural and Environmental Informatics
Raj Gaire | Research Software Engineer
22 October 2013
CSIRO COMPUTATIONAL INFORMATICS
IN COLLABORATION WITH

2. 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.
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3. Kirby ‘Smart’ Farm
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Location
Farm Area:
Smartfarm Area:
Livestock:
Devices:
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Armidale, NSW, Australia
739 Hectares (or 1827 Acres)
269 Hectares (or 665 Acres)
Cattle, Sheep
100 Soil Sensors
2 Weather Stations
Cattle ear tags
Flex, Alix PC, 3G Modem etc.

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5. 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.
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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.
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6. Our Architecture
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7. Smartfarm Ontology
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8. Data Dimensions
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9. 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
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10. Event Detection
Event Description
Web Form
… …. …. ….
… …. …. ….
… . Submit
Event
Manager
Event
Evaluator
Event
VirtualSensor
Message
Queue
Alerts
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Event
Description
Storage
GSN
Storage

11. 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
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14. Visualisation
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15. Future Works
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SPARQL based access to dynamically generated data cubes
Machine Learning over the Data
Integrate satellite data
Social Farming
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16. 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!
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17. Acknowledgement
Kerry Taylor
Laurent Lefort
Michael Compton
David Henry
Ali Salehi
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David Lamb
Gregory Falzon
Derek Schneider
Ashley Saint

18. Thank you
Computational Informatics
Raj Gaire
Research Software Engineer
t +61 2 6216 7090
e raj.gaire@csiro.au
w www.csiro.au/CCI
CSIRO COMPUTATIONAL INFORMATICS