Development and Web-Based Visualization of a Domain-Specific Semantic Model for Coffee Culture and Production Processes

The global agricultural supply chain is a complex network with different data silos, complicated cultivation parameters and highly subjective sensory evaluation metrics. Formal knowledge representation is a must, if real semantic interoperability is to be achieved across this multidimensional domain. This work consists of the rigorous design, implementation and quantitative evaluation of the “Coffee Culture and Production Processes Ontology” and a custom client-side web-application named the “Coffee Ontology Explorer”. The semantic model, developed with standard ontology design principles and the Web Ontology Language (OWL), captures the entire coffee lifecycle from botanical taxonomy and harvesting methods to specific roasting profiles and subjective brewing evaluations. Meanwhile, the React.js based ontology explorer tackles the ongoing limitations of traditional, heavy-client ontology visualization tools by utilizing a browser-native document parsing application programming interface for high efficiency, serverless ontology parsing. The quantitative estimation of the developed semantic model gives a 0.297 relation richness indicator, which confirms a highly interconnected and structurally complex knowledge graph, and not a simple taxonomic flat. Moreover, the performance profiling of the web application shows less than one second for parsing and less than 200 milliseconds for rendering complex graphical data even for large ontological hierarchies. This technical framework is a highly scalable, multilingual semantic infrastructure that is uniquely suited for integration into larger industrial traceability systems and provides a fundamental template for computer scientists, agritech developers, and agricultural stakeholders who want to exploit semantic web technologies for precision agriculture.