The wheat lifecycle involves multiple stages, from crop cultivation and disease monitoring to harvesting, transportation, and storage, each generating vast and heterogeneous data from various devices, platforms, and users. The lack of interoperability between these systems poses a significant challenge to achieving seamless supervision, data integration, and intelligent decision-making across the entire chain. This paper provides a theoretical overview of a conceptual architecture for an ontology-based system intended to supervise and integrate all phases of the wheat production chain, from farm to warehouse. The proposed architecture is conceptually structured into five main layers: data acquisition, semantic integration, artificial intelligence, reasoning and decision support, and user interface. It is designed to resolve semantic and structural heterogeneity in order to achieve interoperability among diverse data sources. It supports real-time data analysis, semantic reasoning, and full traceability through the integration and linking of distributed information. This conceptual work lays the theoretical foundation for the future development of an intelligent, modular, and interoperable platform for sustainable wheat production management. The proposed framework was validated through a dedicated use case on wheat grain quality classification. This validation involved applying deep learning models integrated with semantic ontology support to ensure both accurate prediction and meaningful interpretation of results.