1 / 32

Seishi Ninomiya, Matthew Laurenson and Masayuki Hirafuji National Agricultural Research Center

A working world-compatible grid framework for ubiquitous agricultural and natural resource applications. Seishi Ninomiya, Matthew Laurenson and Masayuki Hirafuji National Agricultural Research Center Tsukuba, Japan. How to make Ag and NR applications that can run anywhere in the world?.

Télécharger la présentation

Seishi Ninomiya, Matthew Laurenson and Masayuki Hirafuji National Agricultural Research Center

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A working world-compatible grid framework for ubiquitous agricultural and natural resource applications Seishi Ninomiya, Matthew Laurenson and Masayuki Hirafuji National Agricultural Research Center Tsukuba, Japan

  2. How to make Ag and NR applications that can run anywhere in the world?

  3. Examples of Ag and NR decisions • Strategic • Which crop or variety to plant • Whether to dam a river • What land use is appropriate • Operational • Whether to spray a crop to protect from disease • How much to irrigate • Where will flooding occur?

  4. Potential for Data Sharing Between DSS Data Decisions

  5. Farm-specific Spray records Chemical properties Data is common to all Weather Dynamic Multiple sources Existing databases Data Characteristics or

  6. Current Situation: Web-based applications but… • Applications closely coupled to databases • Limited range of applications at each site • Duplicated development at each site

  7. Connectors for local data Ag and NR Grid – a “Hotel Room” for Agricultural and Natural Resource DSS User interface in national language Room DSS DSS DSS Soils Maps Weather Elevation

  8. New Zealand Maps Soils Elevation Weather

  9. 日本 地図 地 地面の高さ 気象

  10. 中国 土壤 交通图 气象 海拔

  11. Challenges • Databases differ in format and structure (even if contain the same kind of data) • Internationalization is time consuming • Collecting and maintaining farm-level data is time consuming

  12. Problem 1 • Databases containing the same kind of data (eg weather) differ: • Logical structure • Database software • Access method

  13. Mediated Architecture – “Brokers” Consistent data access to one kind of data “Driver” for each database Domain Databases (Heterogeneous) Applications Relational Broker File-based Web-pages/CGI

  14. We Have Brokers For: • Surface Weather Observations • Digital Elevation Models • Web-based map services • Soils data

  15. Brokers • Provide access to both password-protected and publicly accessible databases • Utilize whatever access approach database owners are willing and able to provide • Java applications access broker via RMI (have alternative HTTP firewall bypass) • Have associated JavaBean components to simplify client application development (access each broker in a separate thread) • Are open source under GNU LGPL

  16. MetBroker • Consistent interface to 15 databases (6000 stations) • SOAP-based access (Visual Basic, Delphi...)

  17. Demo 1 MetBroker-linked spreadsheet

  18. DEMBroker – Digital Elevation Data • World 1km resolution • Japan 50m resolution

  19. ChizuBroker – Online Maps www.mapfan.com(Japan) www.mapzone.co.nz(New Zealand)

  20. Demo 2 – 3D DEM viewer

  21. Problem 2 • Need to be able to localize software

  22. Java ResourceBundle Issues • Maintenance: • Add new item  must recompile • Performance: • Not designed for distributed computing • Java-specific

  23. Web-based User Interface Localization User Supervisor Translator User ResourceServer User Database Translator User

  24. Translator Applet

  25. Problem 3: Collecting and MaintainingFarm-level data

  26. Field Monitoring Server (Fieldserver)

  27. Fieldserver Roles • Field monitoring (weather, camera) • Wireless Internet access point (for PCs, wearable computers...) • Wireless relay • Distributed parallel processing “farm”?

  28. Fieldserver Engine • Sensor-linked Web-server • Ethernet IF for Wi-Fi AP • DDS (Direct Digital Synthesizer) • Remote-controlled I/O

  29. At UCC Coffee Farm, Hawaii

  30. Summary • This approach handles the diverse kinds of data and databases, and diverse data ownership prevalent in agriculture and natural resource management domain.

  31. We welcome interest, comments, collaboration • Fieldserver: http://yummy.narc.affrc.go.jp hirafuji@affrc.go.jp • Brokers: http://www.agmodel.net Thank you

More Related