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Fungi represent a highly nutritious dietary staple that can be essential for supplying cost-effective protein, vitamins, and minerals to populations around the globe; however, their successful cultivation requires extensive training and specialized knowledge to carefully calibrate a range of environmental variables. Internally displaced populations located in Northern Syria depend on extremely limited traditional oyster mushroom farming, which falls short of meeting local consumption requirements. Numerous international and local non-governmental organizations (NGOs) supporting Syrian refugees run mushroom-farming programs. They have encountered considerable difficulties and obstacles in mushroom farming among the intended participants. Thus, the two core questions steering this investigation are: (1) How might organic mushroom farming be advanced via a reliable and low-cost intelligent cultivation system? (2) In what ways can organic mushroom farming methods be streamlined to assist internally displaced persons and Syrian refugees? This investigation examines the mechanization of mushroom farming by designing and implementing an intelligent oyster (Pleurotus ostreatus) cultivation system for remote surveillance and adjustment of environmental conditions—temperature, humidity, air quality, and light—within the growing space. Additionally, ready-made and purpose-built, intuitive web interfaces were created to enable growers to survey and operate their farms via the Internet remotely. Consequently, a trustworthy and economically viable intelligent oyster mushroom cultivation system was designed and successfully implemented in this study. The system encompasses remote surveillance and operation via intuitive interfaces. This makes mushroom farming more straightforward not only for displaced populations and refugees but also for mushroom growers in nations with limited financial resources. This research can aid in the elimination of poverty and hunger, in accordance with the United Nations Sustainable Development Goals one and two.

This paper delivers an interview-based investigation and literature analysis examining the potential benefits and barriers to adopting Artificial Intelligence (AI) in farming enterprises. Three branches of agriculture are examined: crop cultivation, dairy farming, and beef farming. As groundwork for the study, a literature analysis re-evaluates earlier work on intelligent farming. Subsequently, an interview study with 21 participants examines receptivity and readiness for intelligent farming tools and provides technical detail on three selected AI applications in agriculture. Though centered on circumstances in Sweden, the results apply to the wider European agricultural context and beyond. The study uncovers a broad range of elements that both promote and hinder the transition. Chief among the recognized opportunities are the strategic weight intelligent farming carries on the agendas of numerous sector players, the overall shift toward software-as-a-service models through co-owned equipment, the enormous stockpiles of existing data, and the substantial eagerness among farmers for novel technology. Conversely, the study highlights primary obstacles, including technical and legal difficulties related to data ownership, looming cybersecurity threats, the need for a clearly articulated economic rationale, and the occasional lack of technical competence within the sector. The paper finds that the overarching direction signals a shift toward intelligent farming, but the pace of change will depend on how well the identified barriers are resolved.

Smart farming increasingly embeds artificial intelligence, machine learning, predictive analytics, remote sensing, robotics, and automated decision-support systems into agronomic decision-making. These technologies promise more precise, timely, and data-intensive forms of farm management. Their growing role suggests that agronomic decisions are no longer shaped only by farmer observation, seasonal experience, and advisory knowledge, but also by algorithmic interpretation. This article addresses the theoretical problem that AI-supported farming systems may improve technical efficiency while simultaneously reshaping farmer judgment, experiential knowledge, trust, and decision authority. Smart farming is therefore not merely a matter of technological adoption. It is also a reconfiguration of how farming problems are recognised, evaluated, prioritised, and acted upon. The objective of the article is to develop a theory of human–AI decision dependence in smart farming. The theory explains how repeated interaction with algorithmic systems can create new forms of reliance, cognitive offloading, skill transformation, and redistributed authority. It also explains why dependence on AI can be supportive, excessive, or contested depending on the quality of system design, institutional context, and farmer capacity to interpret recommendations. The article contributes a conceptual model of human–AI decision dependence in smart farming. It argues that dependence is not simply a user attitude toward technology but a sociotechnical process through which judgment, skill, trust, evidence, and authority are redistributed. This perspective helps future smart farming research examine not only whether AI systems perform accurately, but also how they change the human capacities and decision relations on which farming depends.

Smart farming has entered a phase in which agricultural fields, machines, livestock systems, greenhouses, and advisory platforms increasingly generate continuous streams of digital information. Sensors promise greater precision, faster monitoring, and more responsive management, yet their proliferation has not automatically improved farmer decision-making. Many farms now face a paradox in which more data are available than can be interpreted, trusted, or acted upon in routine agronomic practice. The central problem addressed in this article is the widening gap between sensor abundance and actionable agronomy. Smart farming systems often excel at data collection but remain less effective at filtering, prioritising, and translating data into decisions that farmers can use under time, labour, weather, and resource constraints. This creates a data-to-decision gap that reduces the practical value of agricultural digitalisation. The objective of the article is to propose a conceptual pathway for reducing data overload in smart farming systems. The pathway moves from sensor data acquisition to information filtering, decision relevance, decision translation, and actionable agronomic delivery. It is designed to help researchers, developers, advisors, and farm managers evaluate not only whether data are collected, but whether they are converted into usable decisions. The proposed pathway identifies four sequential stages: sensor data acquisition, information filtering and relevance assessment, decision translation, and actionable agronomy delivery. Three tables support the framework by cataloguing sensor sources, classifying filtering strategies, and presenting the full pathway. The article concludes that the value of smart farming should be judged less by the amount of data generated and more by the quality, usability, and timeliness of the agronomic decisions enabled.

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.

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.

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.

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.

Data science has become a central analytical foundation of precision agriculture, enabling researchers and practitioners to transform heterogeneous farm, crop, soil, climate, machinery, sensor, and remote-sensing data into decision-relevant knowledge. This original bibliometric and thematic review examines how precision agriculture data science has evolved as a research field, how its knowledge structures are organized, and which methodological and application frontiers are shaping its future direction. The review follows a transparent bibliometric logic by distinguishing database searching, eligibility screening, metadata cleaning, keyword normalization, bibliometric indicators, thematic mapping, and interpretive synthesis. Because verified database-export counts were not available in the present working environment, the manuscript does not report invented publication totals, citation rankings, cluster sizes, or network metrics. Instead, it develops a cautious bibliometric-thematic framework grounded in the approved reference set and in clearly stated methodological safeguards. The review focuses on research evolution from early sensor analytics and remote observation toward machine learning, deep learning, big data, IoT-enabled systems, data fusion, decision support, and sustainable smart farming. It also examines knowledge structures around journals, keywords, co-occurrence logic, thematic clusters, and emerging research fronts. The main contribution is an integrated synthesis of precision agriculture data science as a field shaped by technical innovation, methodological fragmentation, data-quality constraints, interoperability challenges, explainability needs, reproducibility demands, and farmer-centered implementation concerns. The review provides a future agenda for more robust, transparent, usable, and responsible data science in precision agriculture.

Sustainable precision agriculture increasingly depends on the convergence of artificial intelligence, robotics, automation, and Internet of Things technologies rather than on isolated digital tools. This integrative review synthesizes how these technology domains interact across sensing, data transmission, analytics, decision support, robotic or automated actuation, and feedback within sustainability-oriented farming systems. The review uses a curated peer-reviewed literature selection and an integrative evidence-charting logic to map technology domains, agricultural applications, integration mechanisms, decision functions, sustainability relevance, and system-level challenges. The AI focus includes machine learning, deep learning, computer vision, predictive analytics, data fusion, and decision-support models used for crop monitoring, yield estimation, input optimization, pest and disease detection, and risk forecasting. The robotics and automation focus covers autonomous field robots, UAVs, smart machinery, task execution, and automated farm operations. The IoT and sensing focus includes wireless sensor networks, distributed monitoring, machinery telemetry, remote sensing, cloud and edge computing, and data platforms. The synthesis shows that technology convergence may support resource-efficient, climate-sensitive, and more responsive farm management, but only when data quality, interoperability, field robustness, explainability, affordability, governance, usability, and farmer or advisor feedback are addressed. The review contributes a system-level framing of sustainable precision agriculture as a socio-technical feedback system in which digital sensing, AI interpretation, automated action, and human decision-making must be jointly designed. Future research should prioritize validated, interoperable, transparent, and farmer-centered integration pathways rather than assuming that more automation or more data automatically improves sustainability outcomes.

Smart farming increasingly operates through digital platforms that connect farm machinery, sensors, remote-sensing services, advisory systems, agricultural inputs, data analytics, and decision-support applications. Although these arrangements may improve coordination and service integration, their governance implications cannot be evaluated through technological functionality or productivity expectations alone. This article develops an original conceptual model for examining platform governance in smart farming as a socio-technical problem involving data power, platform dependency, value creation, value capture, farmer autonomy, and accountability. The model begins with platform ecosystem mapping, actor identification, and agricultural data-flow analysis. It then assesses how data access, control, portability, interoperability, asymmetry, algorithmic influence, switching costs, and service bundling may shape dependency relationships. A parallel value pathway distinguishes ecosystem value creation from value capture and distribution among farmers, platform operators, technology providers, advisers, suppliers, complementors, cooperatives, and public institutions. Farmer autonomy is examined through decision rights, informed participation, practical agency, human oversight, trust, digital inclusion, and access to contestation and remedy. The model further links identified risks to governance mechanisms such as data stewardship, transparency, auditability, interoperability requirements, participatory governance, farmer representation, accountability structures, and dispute resolution. Its main contribution is a structured framework for evaluating smart farming platforms through power, dependency, distribution, and agency rather than platform performance alone. The model supports conceptual analysis and research design but requires contextual implementation and empirical validation.

Precision agriculture is increasingly shaped by sensors, remote sensing, artificial intelligence, automated machinery, digital platforms, and data-driven decision support. However, technical capability alone does not establish whether a smart farming system is usable, locally appropriate, socially responsible, environmentally beneficial, resilient under disruption, or worthy of farmer reliance. This conceptual article proposes a human-centered precision agriculture framework that places farmers, farm workers, agricultural advisors, local knowledge, farm conditions, usability, accessibility, explainability, decision confidence, human oversight, and responsible governance at the center of system design. The framework integrates sustainability and resilience assessment with user-centered artificial intelligence, participatory design, transparent data practices, uncertainty communication, accountable decision support, and iterative learning. It distinguishes decision confidence from decision quality, explainability from guaranteed trust, participation from assured adoption, and technological potential from demonstrated sustainability or resilience. The proposed structure organizes human-centered smart farming into interconnected stages covering problem definition, stakeholder mapping, farmer-needs assessment, data and system context, sustainability and resilience requirements, explainability design, participation, oversight, governance, evaluation, and feedback. Its main contribution is a socio-technical framework for structuring research and design priorities while maintaining explicit boundaries around empirical validation, farmer autonomy, responsible implementation, and claims of system readiness.

Smart farming technologies are frequently associated with improved efficiency, environmental performance, and adaptive agricultural management, yet digital capability alone does not establish sustainability. This article proposes a sustainability-by-design framework that embeds environmental, economic, social, resilience, and governance considerations into the design and evaluation of smart farming technologies. The framework connects digital agriculture capabilities with resource-use baselines, water- and nutrient-use assessment, conditional pesticide and energy considerations, soil and biodiversity relevance, waste and emissions evaluation, and human oversight. It further integrates climate-risk assessment, scenario planning, adaptive farm-system design, operational resilience, learning feedback, and explicit boundaries against treating monitoring or prediction as proof of climate resilience. Social value is addressed through farmer participation, autonomy, digital inclusion, accessibility, usability, affordability, local relevance, knowledge integration, trust, transparency, data governance, and accountability. Cross-cutting sustainability design principles include context sensitivity, life-cycle thinking, interoperability, trade-off assessment, uncertainty communication, evaluation planning, controlled updating, and clearly defined decision boundaries. The proposed framework is intended to organize design choices, evidence requirements, governance safeguards, and future evaluation rather than prescribe farm-management actions or certify sustainable outcomes. Its main contribution is a structured socio-technical model for examining when, how, for whom, and under what conditions smart farming technologies may support sustainability-oriented agricultural transformation.

Smart farming increasingly operates through interconnected socio-technical ecosystems rather than through isolated technologies deployed at farm level. These ecosystems involve farmers, advisors, technology and platform providers, data intermediaries, equipment and input suppliers, cooperatives, public institutions, research and extension organizations, and agri-food supply-chain actors. This article develops a conceptual ecosystem model for examining how these stakeholders contribute to data-driven agricultural innovation and how value may be created, captured, and distributed through their interactions. The model-building approach integrates stakeholder roles, multidirectional data and knowledge flows, analytics and advisory services, platform coordination, institutional support, co-production, and co-innovation. It distinguishes farmer value, platform value, supply-chain value, public value, knowledge value, and sustainability value while treating value capture and distribution as separate analytical questions. Governance dimensions include data access, control, sharing, interoperability, portability, stewardship, transparency, accountability, trust, participation, digital inclusion, dependency, and power imbalance. The principal contribution is a structured framework that connects innovation processes with governance and distributional boundaries rather than assuming that technological deployment automatically produces shared value or farmer benefit. The model is intended to support conceptual analysis, stakeholder dialogue, and future empirical research. It does not establish the effectiveness, fairness, policy suitability, commercial viability, or implementation readiness of any particular smart farming ecosystem.