An OWL Based Ontology Model for Soils and Fertilizations Knowledge on Maize Crop Farming: Scenario for Developing Intelligent Systems

The exponential growths of electronic data in heterogeneous forms cut across all real-life scenarios and disciplines, agriculture for instance. Besides, the volume and varieties of these data in various repositories across the global space is on one hand a heartwarming development and on the other hand, gradually becoming a challenge in terms of relevant information retrieval as a result of ambiguities in natural languages. Accessing knowledge in respect to soils and fertilizers that can affects maize crop during planting stage is very significant in order to improve and maintain the crop's maximum yields. In lieu of this, a cutting-edge technology that is promising towards mitigating this challenge of retrieving relevant information is by modeling data ontologically. Ontology is a data modeling technique for knowledge representation in a machine understandable format. Therefore, this paper aims to model an OWL-based ontology for soils and fertilization knowledge that can assist in a better knowledge of soil and appropriate measures of fertilizers to apply for maize crop. The domain-based ontology is designed using hybridization of Fox-Gruninger, Methontology and FAO-Based methodologies and written using OWL2 Web Ontology Language RDF/XML syntax. The correctness of the ontology's content and correctness of the ontology development have been constantly validated by the domain experts and via experiments. The proposed system would provide a well-structured knowledge-based system for complex queries on soils and fertilizers knowledge that can affect maize crop in a more accurate and timely information.