Design and implement architecture of knowledge based system to identify water quality for irrigation in the Indian context

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Design and implement architecture of knowledge based system to identify water quality for irrigation in the Indian context

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Abstract

Planning and management of water resources and its optimal use are a matter of urgency for most countries of the world, and even more so for India with a huge population and the need for increased food production is apparent. The suitability of water for irrigation will greatly depend on climatic conditions, physical and chemical properties of soil, salt tolerance of crop grown etc. Also, water plays an important role throughout the food chain, from farms, to food processing plants and finally to consumers' health. It is, therefore, crucial to examine all potential hazards related to the use of water for irrigation by providing knowledge to environment people, planers, researchers and scientists, farmers etc. As knowledge oriented tool, Knowledge Based system can be effectively used for assessing quality of water and thus helps in nation's development. The objective of this paper is to develop working model for better functioning of the specific agro-ecosystem by examining quality and evaluating the quality of water used for irrigation in the area.

Abstract
Introduction
Quality parameters of importance in agricultural use
Architecture of intelligent KBS to identify water quality for irrigation
1. User interface
2. Interface engines
3. Knowledge base
Development of KBS for identifying water quality for irrigation
1. Web user interface
2. Mobile based user interface
Conclusion
References

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[...] http://doi.ieeecomputersociety.org Table Quality criteria for irrigation water Class of irrigation Water Can be Can be Good used with used caution 250-750 142-249 25- 249-426 50 >100 or > 426-710 Quality Criteria Electrical Conductivity dS/m Chlorides mg/l BOD5 Suspended solids pH Temperature Very good Unsuitable (bad) >3,000 >710 0-250 0-142 0 Text Communicator Agent Listening User Interface Explanation Part Knowledge Base -Facts -Rules -Knowledge Methodology Inference Engine -Rule Based -Forward chaining -Backward chaining Knowledge Acquisition Model Deduction Data Source Knowledge Agent -Reasoning -Ontology -Analogy -Representation -Experts -Reports -Database -Creation -Collection -Processing Figure Functional Block Diagram of Architecture of Intelligent KBS KBS to Identify water quality for irrigation This [...]

[...] A mobile agent system design based on the use of XML-based agent code, the UDDI registry for agent registration and lookup/discovery and XML Web Service calls for mobile agent intercommunication and migration. Using the built-in Common Object Request Broker Architecture (CORBA), data and execution requests are passed through back-end Java class and the resulting Java Beans. Communicating with the three lower layers Data, Information, and Knowledge in the proposed architecture, CORBA collects and sends the requested water data. To simplify calls, input and output parameters are converted into XML strings by a customized XML parser. [...]

[...] are included in chemical water quality parameters Classification of water quality of irrigation for important parameters is shown in table ARCHITECTURE OF INTELLIGENT KBS TO IDENTIFY WATER QUALITY FOR IRRIGATION Any new KBS architecture needs to support the automated transformation of data to information and finally to knowledge. In addition, each component needed internal automation to advance their internal processes. Figure 1 describe the top level functional bock diagram of architecture of Intelligent KBS to identify water quality for irrigation as well as crop selection based on it. [...]

[...] This module will provide description to the user of the system that why the system asked for information of parameters and standards of water quality and how the system arrived at a specific conclusion DEVELOPMENT OF KBS FOR IDENTIFYING WATER QUALITY FOR IRRIGATION Web-based as well as Mobile based interface is the primary concentrate on development of this proposed architecture looking to the popularity of them in terms of easy-to-use and availability. Here intelligent agents are used in each component. [...]

[...] Therefore, the knowledge sources have also to be ascertained which could be the experts in the domain itself .But in the development of this KBS to identify water quality of irrigation, as material related to this is not compiled and not available at one source, information and knowledge gathered was acquired from the various sources like books, pollution control booklet, experts etc. The collected knowledge is stored in appropriate way so that it can be saved, inferred, and manipulated. Thereafter, for maintaining consistency of this knowledge base ontology is created and extraction of ontology using sample rule from Knowledge Base is done. [...]

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