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Smart farming is increasingly promoted as a pathway for improving agricultural productivity, resource efficiency, climate responsiveness, and data-driven decision-making. Its core technologies include sensors, digital platforms, machine learning, remote sensing, robotics, automation, and decision-support systems. These tools are often presented as mechanisms for making agriculture more precise, predictable, and adaptive. However, smart farming is not only a technical shift in agricultural tools. It also changes how knowledge is produced, how decisions are justified, how risks are distributed, and how farmers interact with platforms, advisers, suppliers, regulators, and markets. A critical review perspective is therefore needed to examine the social, institutional, political, and ecological consequences of agricultural digitalization. This article critically reviews smart farming as a socio-technical transformation rather than a purely technical modernization process. It examines how datafication, digital governance, farmer adoption, platform dependency, power asymmetry, and sustainability tensions shape the meaning and consequences of smart farming. The central argument is that smart farming should be evaluated through its effects on data control, farmer agency, institutional accountability, and ecological responsibility. The review synthesizes scholarship on digital agriculture, precision agriculture, smart farming, data governance, adoption barriers, farmer-centered challenges, sustainability claims, and responsible innovation. It does not treat technology adoption as a simple matter of farmer willingness or technical readiness. Instead, it situates adoption within broader arrangements of infrastructure, affordability, trust, knowledge, governance, and market pressure. The article contributes a critical synthesis of smart farming evidence and develops a forward-looking agenda for research and governance. It argues that smart farming futures should be designed around transparency, participation, interoperability, accountable digital systems, social equity, and ecological care. The review therefore reframes smart farming as a contested transformation whose benefits depend on how digital systems are governed, adopted, and made accountable.

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 increasingly depends on data generated by sensors, machinery, farm management systems, remote monitoring tools, digital platforms, and decision-support applications. These data flows support agronomic decision-making, resource efficiency, traceability, automation, advisory services, and supply-chain coordination. As farm operations become more data-intensive, farm data becomes a strategic resource rather than a neutral technical by-product. Farm data creates governance challenges because its production, interpretation, exchange, and reuse are distributed across multiple actors and technical systems. Farmers, technology providers, platform operators, agribusiness firms, advisors, researchers, regulators, and supply-chain actors may each hold different forms of access, control, and responsibility. This creates ambiguity around ownership claims, interoperability obligations, privacy safeguards, consent practices, accountability pathways, and farmer agency. This article develops a layered conceptual framework for farm data governance in smart farming ecosystems. The framework conceptualizes farm data governance as an interaction among data production, technical interoperability, ownership and control, privacy and consent, accountability mechanisms, platform governance, institutional oversight, and farmer agency. It argues that these layers must be analysed together because weaknesses in one layer can undermine governance capacity across the ecosystem. The article synthesizes scholarship on digital agriculture, smart farming, data ownership, interoperability, privacy, accountability, platform governance, farmer agency, and responsible innovation. Rather than treating farm data governance as a purely legal or technical matter, it develops a socio-technical model of how data flows, governance responsibilities, and institutional safeguards interact. This approach supports a more integrated understanding of responsible data governance in smart farming. The proposed framework explains how farm data governance can be organized across technical, organizational, legal, ethical, platform, and institutional layers. It highlights the need for interoperable systems, transparent contracts, meaningful consent, privacy-sensitive data practices, accountable platforms, institutional oversight, and farmer participation. The article contributes a farmer-centered governance model for smart farming ecosystems that are data-driven, trustworthy, and accountable.

Precision agriculture increasingly depends on sensor data generated by soil sensors, crop sensors, weather stations, machinery systems, unmanned aerial vehicles, satellite platforms, and farm management information systems. These technologies allow farms to observe spatial and temporal variability with a level of detail that was not previously available in conventional management. However, the existence of more data does not automatically lead to better decisions. Raw sensor observations must be cleaned, calibrated, validated, interpreted, translated into agronomic meaning, connected to decision-support logic, and assessed against the practical realities of farm work. This article develops a decision-intelligence framework for explaining how sensor data becomes actionable farm decisions in data-driven precision agriculture. The central argument is that decision intelligence depends on the full pathway from data capture to action, not on sensing or modeling alone. The article synthesizes scholarship on precision agriculture, smart farming, agricultural sensor systems, data processing, analytical modeling, decision-support systems, farmer judgment, contextual interpretation, and feedback learning. It treats precision agriculture as a human-in-the-loop management process rather than as a purely automated technical pipeline. The proposed framework contributes to precision agriculture scholarship by showing how sensor data can be transformed into decision-ready information, how decision-support recommendations must be interpreted by farmers, and how implemented actions generate feedback for adaptive farm management.

Explainable artificial intelligence is becoming central to precision agriculture because farm-management decisions increasingly depend on algorithmic recommendations generated from sensor, remote-sensing, weather, soil, crop, and machinery data. However, technical performance alone does not make agricultural AI trustworthy. A recommendation may be statistically accurate yet remain unusable if farmers cannot understand why it was produced, how uncertain it is, whether it fits local conditions, or how it can be questioned. This article develops a farmer-centered approach to explainable AI for precision agriculture. It argues that explainability must connect model logic with farmer interpretation, contextual judgment, decision confidence, contestability, accountability, and feedback learning. The proposed perspective treats farmers not as passive recipients of algorithmic advice but as active interpreters, evaluators, and co-decision-makers within human-in-the-loop agricultural systems. By reframing explainable AI as a socio-technical decision-support requirement, the article contributes a foundation for trustworthy algorithmic decision support that is understandable, usable, context-sensitive, and accountable in real farm-management conditions.

Precision farming increasingly depends on diverse agricultural data sources, yet these sources are often discussed as isolated technologies rather than as complementary inputs within decision-support systems. This article develops a conceptual taxonomy of agricultural data sources for precision farming by classifying remote sensing, geospatial layers, IoT sensors, machinery-generated records, climate and weather data, soil data, environmental observations, farm-management records, and farmer knowledge. The taxonomy approach is literature-grounded and organized around data-generation mechanism, spatial and temporal scale, resolution, format, uncertainty, interoperability requirements, ownership concerns, farmer usability, decision-support relevance, and analytics readiness. Rather than treating data as automatically useful, the article argues that each source family has distinct strengths, limitations, validation needs, and practical conditions of use. Remote sensing can support spatial monitoring, IoT sensors can provide high-frequency observations, machinery records can document operational activity, climate and soil data can contextualize crop response, and farmer knowledge can support interpretation, feasibility assessment, and local validation. The contribution is an original data-source taxonomy that helps researchers, advisors, farmers, and technology developers evaluate how agricultural data sources can be combined responsibly for precision farming. The taxonomy supports more coherent data integration, more transparent AI-ready agricultural systems, and more farmer-centered decision support.

Smart farming has become a central direction in digital agricultural transformation, yet adoption among small and medium-sized farms remains uneven because readiness depends on more than technology availability. This article develops a conceptual digital readiness maturity model for smart farming adoption among small and medium-sized farms. The model is literature-grounded and designed to classify farms across progressive readiness stages rather than treating adoption as a binary decision. It integrates core readiness dimensions, including digital infrastructure, connectivity, device and machinery access, data availability and quality, data literacy, digital skills, affordability, perceived usefulness, trust, data privacy and ownership, farm-management routines, advisory support, cooperative networks, institutional support, interoperability, sustainability orientation, and farmer-centered usability. The proposed maturity logic distinguishes five levels: non-digital or initial readiness, basic digital access, connected and data-aware farming, integrated smart farming use, and adaptive learning-oriented smart farming. The article contributes to smart farming adoption research by positioning maturity as a staged, context-sensitive capability-building process. For small and medium-sized farms, the model provides a practical conceptual tool for identifying readiness gaps, prioritizing support interventions, and aligning digital transformation with farm goals, advisory capacity, institutional conditions, and farmer decision contexts.

Precision agriculture is increasingly organized around farm-generated data, connected machinery, remote sensing, digital advisory tools, analytics platforms, and data-sharing arrangements that reshape how decisions are made, value is created, and responsibility is distributed across agricultural systems. This article develops an original policy framework for farm data sovereignty in precision agriculture, treating sovereignty not as a simple ownership claim but as a governance condition shaped by farmer rights, access, control, consent, portability, transparency, interoperability, benefit-sharing, accountability, and institutional oversight. The framework approach is conceptual and literature-grounded, focusing on how farm data move through platform-controlled infrastructures, service contracts, advisory networks, cooperatives, research systems, and commercial analytics environments. The central policy problem is that farmers may generate data essential to precision agriculture while lacking practical ability to understand, access, transfer, audit, contest, or benefit from data and data-derived services. The article identifies key governance dimensions for fair, transparent, and interoperable farm data ecosystems, including contract fairness, platform accountability, data stewardship, privacy, cybersecurity, algorithmic transparency, dispute resolution, and collective governance. Its contribution is to provide a structured policy framework that helps policymakers, farm organizations, cooperatives, technology providers, advisory services, researchers, and platform developers diagnose governance gaps and design data policies that protect farmer agency while enabling responsible data sharing and innovation in precision agriculture.

Artificial intelligence is increasingly embedded in smart farming through decision-support systems that integrate sensor data, remote sensing, machinery data, farm records, and predictive analytics. Yet agricultural decision-making cannot be reduced to automated recommendations because farm decisions are shaped by agronomic uncertainty, local field conditions, economic constraints, environmental goals, labor availability, machinery limitations, farmer experience, advisory relationships, and risk preferences. This conceptual framework article develops a farmer-centered account of human-in-the-loop intelligence for AI decision support in smart farming. The framework positions AI not as a substitute for farmer judgment but as one component of an iterative socio-technical decision process. It emphasizes farmer judgment, experiential knowledge, contextual validation, explainability, uncertainty communication, human oversight, trust calibration, advisor mediation, feedback, accountability, and adaptive decision-making. The article argues that human-in-the-loop intelligence is more than human approval of algorithmic outputs; it involves interpretation, questioning, correction, practical adaptation, and responsibility for situated decisions. By integrating agricultural AI, precision agriculture decision support, farmer knowledge, responsible innovation, and human-centered design perspectives, the manuscript contributes a structured conceptual basis for designing smart farming systems that preserve farmer agency while improving the practical relevance, transparency, and accountability of AI-supported decisions.

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.