Ontology development and query execution for an agro-advisory system
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In agriculture domain, farmers have queries regarding crop, soil, climate, cultivation process, disease, and pest. They express their queries in a natural language which are usually answered by agriculture experts. Due to lake of access, distance or time, the expert is usually not present physically to answer all the queries of the farmers. Hence, the farmers may not understand clearly what the experts wanted to convey. In such situation, there is a possibility of communication gap between farmers and knowledge of agriculture experts. It is desirable to capture agriculture experts’ knowledge in a system that understands farmers’ queries appropriately and gives the recommendations for it. An Agro-Advisory System is developed to fulfil these requirements. It is acknowledged based system. The knowledge base is maintained in the form of ontology. Ontology is integrated with services developed for this system. Ontology contains cotton crop knowledge of Gujarat region. Farmers can ask their queries related to cotton crop cultivation by Android device and get recommendations to improve crop productivity. The system is able to send notification and alert to farmers. Thesis work includes development of ontology for the cotton crop model and corresponding SPARQL (Simple Protocol and RDF Query Language) queries are executed on RDF (Resource Description Framework) data. Simple, complex and reasoning based queries are identified during thesis work.
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