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IoT-enabled precision agriculture connects sensors, devices, communication networks, data platforms, and decision-support systems to support more responsive agricultural monitoring and management. These systems are increasingly discussed in relation to crop monitoring, irrigation management, greenhouse control, livestock monitoring, soil assessment, and farm-level advisory services. Their value, however, depends on how reliably connected technologies function under agricultural field conditions. The evidence base remains fragmented across architecture design, sensor deployment, connectivity models, data processing, and decision-support applications. Some studies emphasize layered IoT architectures, while others focus on wireless sensor networks, cloud platforms, edge computing, communication protocols, or analytics-enabled management systems. This fragmentation makes it difficult to compare implementation models or assess whether technical designs are sufficiently aligned with agricultural decision needs. This article systematically reviews IoT-enabled precision agriculture literature with attention to four core domains: architectures, sensor networks, connectivity models, and decision-support applications. The central review question asks how IoT-enabled precision agriculture has been designed and applied through these domains, and how technical choices shape implementation reliability, scalability, interoperability, energy efficiency, cybersecurity, and decision-support relevance. The review treats IoT not as an automatically beneficial technology, but as a socio-technical system whose usefulness depends on context, integration, and field performance. The review synthesizes peer-reviewed scholarship using explicit search concepts, eligibility criteria, study-selection logic, and evidence-mapping domains. The synthesis is organized around IoT architecture classification, sensor-network typology, connectivity model comparison, decision-support application mapping, implementation barriers, technical limitations, and future research directions. Because record-level search totals and exclusion counts were not available, the study-selection process is described narratively without fabricated PRISMA counts or statistical pooling. The review identifies recurring architectural patterns, sensor-network configurations, communication trade-offs, data-processing approaches, and decision-support applications in IoT-enabled precision agriculture. It also highlights persistent limitations involving interoperability, deployment scale, energy demand, data quality, field validation, cybersecurity, usability, and farmer-centered implementation. The article contributes an evidence-focused synthesis for designing more reliable, scalable, secure, and agriculturally meaningful IoT-enabled precision agriculture systems.

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.