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

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Semantic Web Enabled Smart Farming

  1. 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. 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. 2 | Presentation title | Presenter name
  3. 3. 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. 4. 4 | Presentation title | Presenter name
  5. 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. • • • • 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
  6. 6. Our Architecture 6 | Presentation title | Presenter name
  7. 7. Smartfarm Ontology 7 | Presentation title | Presenter name
  8. 8. Data Dimensions 8 | Presentation title | Presenter name
  9. 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 9 | Presentation title | Presenter name
  10. 10. 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
  11. 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 11 | Presentation title | Presenter name
  12. 12. 12 | Presentation title | Presenter name
  13. 13. 13 | Presentation title | Presenter name
  14. 14. Visualisation 14 | Presentation title | Presenter name
  15. 15. 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
  16. 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! 16 | Presentation title | Presenter name
  17. 17. Acknowledgement Kerry Taylor Laurent Lefort Michael Compton David Henry Ali Salehi 17 | Presentation title | Presenter name David Lamb Gregory Falzon Derek Schneider Ashley Saint
  18. 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

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