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Development of web-based decision support system for field-based crop management

Published:07 November 2007Publication History

ABSTRACT

A web-based decision support system, GZ-AgriGIS, was developed to assist local farmers in the region of Guangzhou, China make field-based crop management decisions, e.g. fertilizer applications (replacement) and irrigation. The system was aimed to share available site-specific agricultural domain knowledge and analytical models with the local farmers. The representation of domain knowledge and organization of analytical models were outlined. Considering the micro-environmental variations of the region, we paid special attention to the spatial suitability of domain knowledge and analytical models. Through the online service of GZ-AgriGIS, farmers can easily acquire scientific guidance for crop management decisions.

References

  1. Runquist, S., Zhang, N., Taylor, R. K., 2001. Development of a field-level geographic information system. Comput. Electron. Agric. 31, 201--209.Google ScholarGoogle ScholarCross RefCross Ref
  2. El-Najdawi, M. K., Anthony C. Stylianou, 1993. Expert support systems: integrating AI technologies. Communications of the ACM 36(12), 54--63 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Batanov, D. N., and Zhuang, C., 1995. An object-oriented expert system for fault diagnosis in the ethylene distillation process. Computers in Industry, 27(3), 237--249. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Development of web-based decision support system for field-based crop management

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        cover image ACM Other conferences
        GIS '07: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
        November 2007
        439 pages
        ISBN:9781595939142
        DOI:10.1145/1341012

        Copyright © 2007 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 November 2007

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        Overall Acceptance Rate220of1,116submissions,20%

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