Semantic Web Enabled Smart Farming
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Semantic Web Enabled Smart Farming

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Slides from my talk at 1st International Conference on Semantic Machine Learning and Linked Open Data (SML2OD) for Agriculture and Environmental Informatics

Slides from my talk at 1st International Conference on Semantic Machine Learning and Linked Open Data (SML2OD) for Agriculture and Environmental Informatics

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Semantic Web Enabled Smart Farming Semantic Web Enabled Smart Farming Presentation Transcript

  • 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
  • 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. 2 | Presentation title | Presenter name
  • Kirby ‘Smart’ Farm • • • • • Location Farm Area: Smartfarm Area: Livestock: Devices: 3 | Presentation title | Presenter name 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.
  • 4 | Presentation title | Presenter name
  • 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. 5 | Presentation title | Presenter name
  • Our Architecture 6 | Presentation title | Presenter name
  • Smartfarm Ontology 7 | Presentation title | Presenter name
  • Data Dimensions 8 | Presentation title | Presenter name
  • 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 9 | Presentation title | Presenter name
  • Event Detection Event Description Web Form … …. …. …. … …. …. …. … . Submit Event Manager Event Evaluator Event VirtualSensor Message Queue Alerts 10 | Presentation title | Presenter name Event Description Storage GSN Storage
  • 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 11 | Presentation title | Presenter name
  • 12 | Presentation title | Presenter name
  • 13 | Presentation title | Presenter name
  • Visualisation 14 | Presentation title | Presenter name
  • Future Works • • • • SPARQL based access to dynamically generated data cubes Machine Learning over the Data Integrate satellite data Social Farming 15 | Presentation title | Presenter name
  • 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! 16 | Presentation title | Presenter name
  • Acknowledgement Kerry Taylor Laurent Lefort Michael Compton David Henry Ali Salehi 17 | Presentation title | Presenter name David Lamb Gregory Falzon Derek Schneider Ashley Saint
  • 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