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Application of web ontology Application of web ontology to harvest estimation of rice to harvest estimation of rice
in Thailandin Thailand
Takuji Kiura*Takuji Kiura*Hitoshi. Toritani**Hitoshi. Toritani**Daisuke Horyu*Daisuke Horyu*
Atsushi Yamakawa*Atsushi Yamakawa*Seishi Ninomiya*Seishi Ninomiya*
*National Agriculture Research Center*National Agriculture Research Center**National Institute for Agro-Environmental Sc**National Institute for Agro-Environmental Sc
ienceience(Japan)(Japan)
Field Server II (NARC)Field Server II (NARC)Web based Wi-Fi network sensor nodeWeb based Wi-Fi network sensor node
• CaseCase Acryl resinAcryl resin
• CoreCore Field Server-Engine or Field Server-Engine or PICNICPICNIC
• SensorsSensors Temperature, Humidity, Temperature, Humidity, PPFDPPFD Soil moisture, Leaf-Soil moisture, Leaf-wetnesswetness UV, IR UV, IR CO CO22 Camera, Camera, MicroscopeMicroscope
• Data-collection and AIData-collection and AI Fieldserver-AgentFieldserver-Agent
• NetworkingNetworking Wi-Fi AP, Fieldserver-Wi-Fi AP, Fieldserver-GatewayGateway
• GRIDGRID MetBrokerMetBroker
Problems of Field Server Problems of Field Server DataData
• Different Type of Sensors, Time Different Type of Sensors, Time ResolutionResolution– Described in XML files (w/o Described in XML files (w/o
standard)standard)• Semantic ProblemsSemantic Problems
– Sensors are added or removedSensors are added or removed– Time resolution may be changed.Time resolution may be changed.
• Small data size (1KB~10MB) for Small data size (1KB~10MB) for each.each.
Problems of old MetBrokerProblems of old MetBroker
• Adding new databasesAdding new databases– writing new DB wrapperswriting new DB wrappers– Restarting MetBrokerRestarting MetBroker
• Adding new observation ItemsAdding new observation Items– Data Modeling for each itemData Modeling for each item– Writing new data object for each itemWriting new data object for each item
New MetBrokerNew MetBroker
BrokerBroker
Decision-Making Support ServicesDecision-Making Support Services Operational ProductsOperational Products Simulation ModelsSimulation Models Detailed Digital ForecastDetailed Digital Forecast
Inference Engine
DB WrapperDB Wrapper
Basic VocabularyItem Definition
OWL
Station metadata RDBMS
Metadata database
Meteorological databases
DB WrapperDB Wrapper
DB WrapperDB Wrapper
2. Request
3. Request
metadata
4. Request data
1. Register
New MetBrokerNew MetBroker
• Adding new databasesAdding new databases– writing Item Definition & Station writing Item Definition & Station
MetadataMetadata
• Adding new observation ItemsAdding new observation Items– Adding new items to basic vocabralyAdding new items to basic vocabraly
•New MetBrokerNew MetBroker can integrate Field can integrate Field Server data with other weather Server data with other weather database.database.
•New MetBrokerNew MetBroker can integrate other can integrate other point observation data (Ex. data from point observation data (Ex. data from other passive sensor networks).other passive sensor networks).
• I18n of client is easier.I18n of client is easier.
• Possibility for personalization.Possibility for personalization.
Advantages of New Advantages of New MetBrokerMetBroker
Develop data integration Develop data integration environment for small and environment for small and distributed agricultural distributed agricultural databasesdatabasesIs OWL/RDF is useful Is OWL/RDF is useful • In other problem domains?In other problem domains?
– Remote Sensing data, Maps, DEMs, etc.Remote Sensing data, Maps, DEMs, etc.
• To integrate different kinds of data?To integrate different kinds of data?– Can we find key property or not?Can we find key property or not?
•Ex. Location is key property to integrate crop experiEx. Location is key property to integrate crop experimental data and Meteorological datamental data and Meteorological data
•1:1 corresponding is not expected 1:1 corresponding is not expected
Use caseUse case
• harvest estimation of rice in Thailandharvest estimation of rice in Thailand– What kinds of data are required?What kinds of data are required?– From where we can get data?From where we can get data?– How to get them?How to get them?
What kinds of data are What kinds of data are required?required?
• Satellite Sensing DataSatellite Sensing Data
• Digital Elevation ModelsDigital Elevation Models
• Thematic mapsThematic maps– Land usage, Land cover, Irrigated FieldLand usage, Land cover, Irrigated Field
• Field Survey DataField Survey Data
• Ground TruthGround Truth
• Meteorological DataMeteorological Data
• Rice ModelsRice Models
North-East Thailand
World cloud free imageNASA Visible Earth(2001)(MODIS satellite image)
Total land: 169×103 km2 in NET(513×103 km2) in Whole Kingdom
Global Digital Elevation Model(GTPO30)USGS EROS data (1996)
Farm holding land : 91.9×103 km2
(208.6×103 km2)Number of household: 2,621×103
Paddy land: 60.7×103 km2
Forest land: 25.3×103 km2
(Agricultural Statistics of Thailand, 2001)
Field Survey data, Ground TruthField 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
From Where we can get From Where we can get data?data?• Satellite Sensing Data, DEM, Thematic mapsSatellite Sensing Data, DEM, Thematic maps
– NASA, NOAA, JAXA, MAFFIN, etc.NASA, NOAA, JAXA, MAFFIN, etc.– Commercial Data ProvidersCommercial Data Providers
• Field Survey DataField Survey Data– Field Survey by our selvesField Survey by our selves– LiteratureLiterature
• Ground TruthGround Truth– Data archive of Sensor NetworkData archive of Sensor Network– Field SurveyField Survey
• Meteorological DataMeteorological Data– Data archive of Sensor Network, Meteorological databases (MetBroker)Data archive of Sensor Network, Meteorological databases (MetBroker)
• Rice ModelsRice Models– ??
How to get data?How to get data?
• Satellite Sensing Data, DEM, Thematic mapsSatellite Sensing Data, DEM, Thematic maps– Web Interface (HTTP POST)Web Interface (HTTP POST)
• Field Survey DataField Survey Data– Field Survey by our selves (XQuery)Field Survey by our selves (XQuery)– Literature (XQuery)Literature (XQuery)
• Ground TruthGround Truth– Data archive of Sensor Network (HTTP GET)Data archive of Sensor Network (HTTP GET)– Field Survey (XQuery)Field Survey (XQuery)
• Meteorological DataMeteorological Data– Data archive of Sensor Network, Meteorological databases (MetBroker)Data archive of Sensor Network, Meteorological databases (MetBroker)
• Rice ModelsRice Models– ??
How to get?How to get?
Name of Dataset
Name of Satellite
Name of Sensor
We need relation between satellite image sensing ontology
and agricultural ontology
Data Integration in Agriculture
WeatherData
Ground Truth
Crop Calendar
When they cultivate rice in Thailand?
Actual Status?
How about weather during rice grow
Ontology RegistryOntology Registry
• Database of Domain Specific OntologyDatabase of Domain Specific Ontology– Resist, Update, Retrieve, Resist, Update, Retrieve, Version ControlVersion Control
• Multi-Ontology Service Multi-Ontology Service – Integrated Ontology?Integrated Ontology?
Ontology Registry 1
2Find Suitable Ontology検索
Add, Delete, Modify
3
New Ontology
I need ontology to solve my
problem…
Ontology RegistryOntology Registry
• Database of Domain Specific OntologyDatabase of Domain Specific Ontology– Resist, Update, Retrieve, Resist, Update, Retrieve, Version ControlVersion Control
• Multi-Ontology Service Multi-Ontology Service – Integrated OntologyIntegrated Ontology
Ontology Registry 1
2Find Suitable Ontology検索
Add, Delete, Modify
3
New Ontology
I need ontology to solve my
problem…
Ontology Registry
Application
1. Developing
Register Ontology
Application
OWLRDF
OWLRDF
(local)
ResourceEditor
2. Localization
(part)
XMLData
(local)
3. Local Utilizes
Local Application A
Local Application B
Local Application C
Local Application D
4. Data Integration
Ontology Metadata
XMLData(A)
XMLData(B)
RelatedDB
Field Survey Data Field Survey Data IntegrationIntegration
Abnormal Weather / Climate Change / Wind & Flood Damage / Food &Water Crisis → Poverty,Health,Safety
Understanding and Prediction of Earth system Understanding and Prediction of Earth system
Observation data is insufficientStandardization/quality/archive/
Metadata/format/open to the public/real time
Data integration is insufficientVarious kinds/
non-homogeneity/huge volume/time gap
Data sharing is insufficientTranslation Data to information
Information network
?
?
Observation data is insufficientStandardization/quality/archive/
Metadata/format/open to the public/real time
Data integration is insufficientVarious kinds/
non-homogeneity/huge volume/time gap
Data sharing is insufficientTranslation Data to information
Information network
Observation data is insufficientStandardization/quality/archive/
Metadata/format/open to the public/real time
Data integration is insufficientVarious kinds/
non-homogeneity/huge volume/time gap
Data sharing is insufficientTranslation Data to information
Information network
?
?
?
?Create new Knowledge for Earth System Science
For Social Needs
Create new Knowledge for Earth System Science
For Social Needs
Numerical prediction
Flood prediction
Cooperationwith international
Organizations
Cooperationwith international
Organizations
Metrologicalinformation
Better use of data
Agriculturemanagement
Disperseduse
Predictionneeds
IntegratedInformation
Numerical prediction
Flood prediction
Cooperationwith international
Organizations
Cooperationwith international
Organizations
Metrologicalinformation
Better use of data
Agriculturemanagement
Disperseduse
Predictionneeds
IntegratedInformation
data
IntegratedInformation
Oceanic data
Flood data
Earth observation dataCollection/quality management
Cooperation with International Research Center
Satellite data
Ground observation data
Cooperation with Earth ObservationPlans
Integration method
data
IntegratedInformation
Oceanic data
Flood data
Earth observation dataCollection/quality management
Cooperation with International Research Center
Satellite data
Ground observation data
Cooperation with Earth ObservationPlans
Integration method
Centralized Data Integration & Fusion①data archive, storage, integration②data assimilation, visualization, mining③design for database schema④data collection and distribution⑤sensor data fusion
Integration of Distributive Data
①constriction of ontology registry
②service by ontology registry③distinguished DB for agriculture④distinguished DB for satellite data
Core System
Centralized Data Integration & Fusion①data archive, storage, integration②data assimilation, visualization, mining③design for database schema④data collection and distribution⑤sensor data fusion
Integration of Distributive Data
①constriction of ontology registry
②service by ontology registry③distinguished DB for agriculture④distinguished DB for satellite data
Centralized Data Integration & Fusion①data archive, storage, integration②data assimilation, visualization, mining③design for database schema④data collection and distribution⑤sensor data fusion
Integration of Distributive Data
①constriction of ontology registry
②service by ontology registry③distinguished DB for agriculture④distinguished DB for satellite data
Core System