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Smart farming platforms are becoming central infrastructures for digital agriculture because they connect field-level sensing, machinery records, farm-management information systems, remote sensing inputs, advisory services, analytics modules, and decision-support tools. Yet many platforms remain limited by fragmented data flows, incompatible formats, weak semantic alignment, uneven governance arrangements, and poor integration with farm decision workflows. This article develops an interoperable reference architecture for smart farming platforms. The approach is conceptual and architecture-oriented: it synthesises platform, IoT, farm-management, interoperability, analytics, and governance concerns into a layered architecture that clarifies how heterogeneous agricultural data and services can be connected, interpreted, governed, analysed, and reused. The proposed architecture includes physical sensing and machinery, connectivity and edge computing, data ingestion, APIs and integration, data standardisation and semantic interoperability, storage and governance, analytics and AI, decision-support and visualisation, farm-management workflow, stakeholder interface, security and access control, and monitoring and feedback layers. The contribution is a structured architecture that distinguishes data connectivity, API access, syntactic compatibility, semantic interoperability, governance, analytics integration, and workflow usability. The article argues that interoperable smart farming platforms should not be treated as simple data repositories or dashboards; rather, they require modular, auditable, secure, and context-sensitive integration across technical and organisational boundaries. The architecture supports researchers, platform developers, machinery manufacturers, FMIS providers, advisors, cooperatives, and policymakers in designing more coherent digital agriculture ecosystems.

Smart farming platforms are frequently conceived around persistent connectivity, modern devices, scalable cloud services, specialized technical support, and the financial capacity to adopt integrated digital systems. These assumptions may not hold in smallholder and connectivity-constrained agricultural settings, where infrastructure gaps, uneven digital skills, limited advisory access, maintenance difficulties, data costs, language diversity, and uncertain platform governance shape whether digital services are usable or sustainable. This article develops a conceptual design science framework for low-resource smart farming platforms. The framework links problem relevance, contextual requirement definition, design principles, platform artifact logic, implementation conditions, evaluation needs, feedback, and framework updating. It focuses on affordability, transparent costs, incremental adoption, modular architecture, interoperability, offline-first operation, low-bandwidth communication, local usability, accessible interaction, advisory integration, maintenance capacity, farmer data control, and trust. The proposed approach treats smallholders as heterogeneous users operating within diverse production systems, infrastructures, institutions, and knowledge networks. Platform functions are therefore organized as configurable responses to identified conditions rather than as a universal technology package. The principal contribution is a structured design science logic for translating low-resource agricultural problems into bounded platform requirements and evaluable conceptual artifacts. The framework can guide platform design and research planning, but it does not independently demonstrate affordability, inclusion, adoption, scalability, sustainability, operational safety, or deployment readiness. These outcomes require local usability assessment, affordability analysis, governance review, maintenance evaluation, implementation research, and field validation involving intended users and supporting institutions.