Crop Disease Analysis Using Ontology and SPARQL

Abstract

Due to the growth of population, it is essential to improve farm productivity to meet the rapidly growing demand for food across the world. Technologies have been used widely to increase the yielding of crops for the highly growing population. Bangladesh has an agriculture based economy. Though majority population of the country lives on agriculture, but sadly, very few of them are aided with the blessings of technology. The knowledge of different factors like weather condition, soil type and particular attributes of the crops to plant is essential to improve the productivity of agricultural crops. The proper management and easy accessibility of this knowledge can increase the production of crops and decrease the loss of crops due to these natural factors. Formally represented knowledge is based on a conceptualization. Ontology is an explicit specification of a conceptualization. It defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Ontology driven applications face the challenge of integrating multiple formats with the information stored in ontologies. In this project, we have proposed an ontology-based knowledge management methodology for the diagnosis of soybean diseases using Protégé and SPARQL. A set of new ontology concepts and properties has been defined to build the conceptual model for the attributes that describes the soybean plant, the factors that defines the soybean diseases and the relation between them. Finally, we develop a web application for soybean disease analysis using a SPARQL endpoint at the backend.