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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.

Digital twins are increasingly discussed as a next step in smart farming because they promise to connect physical agricultural systems with digital representations, sensor data, simulation models, analytics, and decision-support functions. However, the agricultural literature remains uneven in how it defines, architects, enables, and applies digital twins across crop, livestock, greenhouse, irrigation, and farm-management systems. This original scoping review maps the peer-reviewed literature on digital twins in smart farming with attention to architecture types, enabling technologies, data requirements, application domains, implementation challenges, and research gaps. The review follows a scoping logic rather than an effect-estimation or performance-benchmarking design: it charts how studies conceptualize digital twins, distinguishes full or emerging digital twin capability from static models and monitoring dashboards, and classifies evidence by architecture, technology, data pipeline, analytics function, feedback mechanism, implementation stage, and limitation. The synthesis shows that agricultural digital twins are commonly positioned as cyber-physical and decision-support infrastructures that may combine IoT sensing, remote sensing, machinery and environmental data, cloud or edge computing, simulation, AI analytics, visualization, and farm-management interfaces. Application clusters are strongest in controlled-environment agriculture, irrigation and water management, crop monitoring, aquaponics, livestock monitoring, greenhouse visualization, and emerging optimization or automation settings. Across the mapped literature, the main contribution is a structured classification of digital twin architectures and application patterns while preserving conceptual caution: real-time data, AI prediction, or visualization alone does not create a digital twin. The review identifies future research needs in architecture standardization, semantic interoperability, field validation, real-time updating, uncertainty communication, data governance, farmer-centered usability, scalability, and sustainability assessment.

Edge Artificial Intelligence and Tiny Machine Learning are increasingly discussed as approaches for moving selected analytical functions from remote cloud infrastructure to low-power devices located closer to agricultural data sources. Their potential relevance to precision agriculture lies in supporting local sensing, classification, monitoring, and decision-assistance functions where continuous connectivity, high-performance computing, or extensive data transmission may be impractical. This framework-oriented review examines Edge AI and TinyML through four connected dimensions: low-power intelligence, embedded farm systems, agricultural deployment constraints, and farm-scale applications and implementation barriers. The review distinguishes on-device inference and near-sensor intelligence from conventional agricultural Internet of Things sensing, generic wireless sensor networks, cloud-based artificial intelligence, and non-embedded machine-learning applications. It organizes the reviewed evidence around embedded hardware, model compression, memory, latency, energy use, sensor quality, connectivity, environmental exposure, calibration, maintenance, usability, affordability, local validation, cybersecurity, privacy, and human oversight. The synthesis indicates that compact inference can reduce reliance on continuous cloud access in appropriately designed systems, but miniaturized models alone do not establish accuracy, robustness, affordability, usability, sustainability, or deployment readiness. Farm-scale usefulness remains conditional on the quality of sensing systems, hardware-aware model design, environmental testing, lifecycle maintenance, locally relevant validation, and clear responsibility for system oversight. The main contribution is a framework that connects technical design choices with deployment conditions, implementation boundaries, and a research and practice agenda for evaluating low-power agricultural intelligence without converting prototype feasibility into unsupported operational claims.