This document discusses using ontologies and semantic web technologies to integrate heterogeneous agricultural data sources for estimating rice harvests in Thailand. It describes using an ontology registry and integrated ontologies to combine satellite imagery, digital elevation models, land use maps, field survey data, meteorological data, and rice growth models. The goal is to develop a data integration environment to estimate rice harvests using small, distributed agricultural databases from various sources.

1. Application of web ontology to harvest estimation of rice in Thailand Takuji Kiura* Hitoshi. Toritani** Daisuke Horyu* Atsushi Yamakawa* Seishi Ninomiya* *National Agriculture Research Center **National Institute for Agro-Environmental Science (Japan)

2. Backgrounds

3. Field Server II (NARC) Web based Wi-Fi network sensor node Case Acryl resin Core Field Server-Engine or PICNIC Sensors Temperature, Humidity, PPFD Soil moisture, Leaf-wetness UV, IR CO 2 Camera, Microscope Data-collection and AI Fieldserver-Agent Networking Wi-Fi AP, Fieldserver-Gateway GRID MetBroker

4. Problems of Field Server Data Different Type of Sensors, Time Resolution Described in XML files (w/o standard) Semantic Problems Sensors are added or removed Time resolution may be changed. Small data size (1KB~10MB) for each.

5. Problems of old MetBroker Adding new databases writing new DB wrappers Restarting MetBroker Adding new observation Items Data Modeling for each item Writing new data object for each item

6. New MetBroker Broker Decision-Making Support Services Operational Products Simulation Models Detailed Digital Forecast Inference Engine DB Wrapper Basic Vocabulary Item Definition OWL Station metadata RDBMS Metadata database Meteorological databases DB Wrapper DB Wrapper 2. Request 3. Request metadata 4. Request data 1. Register

7. New MetBroker Adding new databases writing Item Definition & Station Metadata Adding new observation Items Adding new items to basic vocabraly

8. New MetBroker can integrate Field Server data with other weather database. New MetBroker can integrate other point observation data (Ex. data from other passive sensor networks). I18n of client is easier. Possibility for personalization. Advantages of New MetBroker

9. Develop data integration environment for small and distributed agricultural databases Is OWL/RDF is useful In other problem domains? Remote Sensing data, Maps, DEMs, etc. To integrate different kinds of data? Can we find key property or not? Ex. Location is key property to integrate crop experimental data and Meteorological data 1:1 corresponding is not expected

10. Use Case

11. Use case harvest estimation of rice in Thailand What kinds of data are required? From where we can get data? How to get them?

12. What kinds of data are required? Satellite Sensing Data Digital Elevation Models Thematic maps Land usage, Land cover, Irrigated Field Field Survey Data Ground Truth Meteorological Data Rice Models

13. North-East Thailand World cloud free image NASA Visible Earth(2001) (MODIS satellite image) Total land: 169×10 3 km 2 in NET (513×10 3 km 2 ) in Whole Kingdom Global Digital Elevation Model(GTPO30) USGS EROS data (1996) Farm holding land : 91.9×10 3 km 2 (208.6×10 3 km 2 ) Number of household: 2,621×10 3 Paddy land: 60.7×10 3 km 2 Forest land: 25.3×10 3 km 2 (Agricultural Statistics of Thailand, 2001)

14. North-East Thailand

15. Field Survey data, Ground Truth Paddock with enough water: transplanting Neighboring paddock: direct seeding another paddock in the same field: abandoned In Khon Kaen, wet season of 2005

16. From Where we can get data? Satellite Sensing Data, DEM, Thematic maps NASA, NOAA, JAXA, MAFFIN, etc. Commercial Data Providers Field Survey Data Field Survey by our selves Literature Ground Truth Data archive of Sensor Network Field Survey Meteorological Data Data archive of Sensor Network, Meteorological databases (MetBroker) Rice Models ?

17. How to get data? Satellite Sensing Data, DEM, Thematic maps Web Interface (HTTP POST) Field Survey Data Field Survey by our selves (XQuery) Literature (XQuery) Ground Truth Data archive of Sensor Network (HTTP GET) Field Survey (XQuery) Meteorological Data Data archive of Sensor Network, Meteorological databases (MetBroker) Rice Models ?

18. How to get? Name of Dataset Name of Satellite Name of Sensor We need relation between satellite image sensing ontology and agricultural ontology

19. Data Integration in Agriculture Weather Data Ground Truth Crop Calendar When they cultivate rice in Thailand? Actual Status? How about weather during rice grow

20. Ontology Registry

21. Ontology Registry Database of Domain Specific Ontology Resist, Update, Retrieve, Version Control Multi-Ontology Service Integrated Ontology? Ontology Registry 1 2 Find Suitable Ontology 検索 Add, Delete, Modify 3 New Ontology I need ontology to solve my problem…

22. Ontology Registry Database of Domain Specific Ontology Resist, Update, Retrieve, Version Control Multi-Ontology Service Integrated Ontology Ontology Registry 1 2 Find Suitable Ontology 検索 Add, Delete, Modify 3 New Ontology I need ontology to solve my problem…

23. Ontology Registry Application 1. Developing Register Ontology Application OWL RDF OWL RDF (local) Resource Editor 2. Localization (part) XML Data (local) 3. Local Utilizes Local Application A Local Application B Local Application C Local Application D 4. Data Integration Ontology Metadata XML Data (A) XML Data (B) Related DB Field Survey Data Integration

24. No Result!! Still Under Development

25. This research is a part of Global Environmental Data Integration Project

27. Thank you for your attention New MetBroker Demo http://www.agmodel.org/metbroker/demo.html