Goldstein, A. - Department of Industrial Engineering and Management, Ariel University, Ariel, Israel;
Fink, L. - Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er-Sheva, Israel;
Raphaeli, O. - Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er-Sheva, Israel;
Ravid, G. - Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er-Sheva, Israel
Farmers, who have to decide which pesticide to use against a particular crop-damaging pest, need to take into account country-specific regulations (e.g. permitted levels of pesticide residues), application instructions and financial considerations. The fact that these data are stored in different locations, sometimes using different terminology or different languages, makes it difficult to gather these data and requires that farmers are familiar with the variety of terms used, which consequently hampers the efficiency and effectiveness of the decision process. To overcome these challenges, a Web application for pest control is proposed to facilitate the integration of information coming from different Internet sources and representing different terminologies by using an ontology. The application is based on a pest-control ontology (formal representations of domain knowledge that can be interpreted by computers) that accounts for various pesticide regulations of different countries to which the crop is exported. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. The pest-control ontology developed in the current research includes pest-control concepts that have yet to be covered by existing ontologies. It is demonstrated in the specific case of pepper in Israel. The ontology is expressed using Web Ontology Language (OWL) and thus can be shared on the Web and reused by other ontologies and systems. In addition, a comprehensive method for developing and evaluating agricultural ontologies is presented.
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