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Artificial intelligence is increasingly used across agriculture and food systems for precision farming, yield prediction, pest detection, supply-chain optimisation, quality control, decision support, and resource management. These applications are often promoted as tools for increasing productivity, reducing uncertainty, improving resource efficiency, and strengthening food-system resilience. As AI becomes embedded in farms, supply chains, laboratories, and food governance infrastructures, its consequences extend beyond technical performance. The central problem is that the benefits of agri-food AI are frequently assessed through accuracy, automation efficiency, profitability, or optimisation metrics, while broader ethical, equity, environmental, and governance implications receive less systematic attention. AI systems may alter farmer autonomy, labour relations, data ownership, market participation, environmental decision-making, and institutional accountability. Treating these consequences as secondary risks understates the degree to which AI changes relationships among people, technologies, land, food, and power. This critical review examines responsible AI in agriculture and food systems through four connected dimensions: ethics, equity, sustainability, and governance. It argues that responsible AI cannot be reduced to technical reliability or model performance because agri-food systems are socially embedded, ecologically consequential, and institutionally fragmented. The article therefore evaluates responsible AI as a socio-technical and governance challenge rather than as a purely computational problem. The article contributes a critical argument that responsible agri-food AI requires accountable design, inclusive access, sustainability-aware evaluation, and multi-level governance. It emphasises that trustworthy AI in agriculture and food systems depends on who controls data, who benefits from automation, who bears environmental and social risks, and who participates in decision-making. Responsible AI is therefore positioned as a core condition for equitable, sustainable, and publicly legitimate agri-food transformation.

Machine learning is increasingly applied across agri-food systems to support prediction, classification, optimisation, monitoring, and decision-making. These applications now extend from farm-level production to processing, logistics, consumption, and food-waste management. As data-intensive agriculture expands, machine learning is often presented as an enabling technology for sustainable agri-food transitions. Despite this promise, the sustainability evidence base remains fragmented. Many studies report improvements in model accuracy, prediction performance, or operational decision support, but they do not always specify how these technical gains translate into environmental, economic, or social outcomes. This creates uncertainty about whether machine learning contributes to sustainability transitions directly, indirectly, or only under particular implementation conditions. This systematic review examines how machine learning contributes to sustainable agri-food transitions through environmental, economic, and social impact pathways. It focuses on the relationship between machine learning methods, agri-food applications, and sustainability-relevant outcomes rather than treating model performance as an end in itself. The review asks where evidence is strongest, where it remains indirect, and how different pathways vary across production, processing, distribution, consumption, waste reduction, and governance. The review finds that environmental and economic pathways are more developed than social and equity pathways. Evidence is strongest where machine learning supports precision input management, yield forecasting, irrigation decision support, operational efficiency, supply-chain analytics, and food-waste reduction. However, stronger longitudinal evaluation, causal assessment, life-cycle integration, participatory design, and equity-sensitive governance are needed before machine learning can be judged as a reliable driver of sustainable agri-food transitions.

Foundation models and multimodal artificial intelligence are beginning to reshape agricultural AI by enabling systems that can integrate images, text, sensor streams, remote-sensing data, environmental records, and management information. These approaches differ from earlier narrow models because they can reuse learned representations across related tasks and support richer forms of agricultural interpretation. Their potential relevance spans crop monitoring, disease detection, advisory services, automation, forecasting, and agri-food decision support. Despite this promise, the transfer of large-scale AI systems into agriculture remains complex. Agricultural environments are biologically variable, spatially heterogeneous, seasonally dynamic, and shaped by management decisions that are often poorly captured in available datasets. As a result, questions remain about data quality, annotation adequacy, domain adaptation, validation, explainability, governance, and deployment risk. This scoping review maps emerging applications, data requirements, and deployment risks associated with foundation models and multimodal AI in agriculture and agri-food systems. It focuses on how the reviewed literature frames model architectures, data modalities, application domains, validation needs, and governance concerns. The purpose is not to estimate pooled effectiveness but to chart the evidence landscape and clarify where knowledge is advancing or remains unsettled. The review uses a scoping approach to organise literature around key concepts, model types, data inputs, agricultural tasks, implementation settings, and responsible deployment concerns. It charts relationships among remote-sensing foundation models, vision-language systems, large language models, generative AI, multimodal data fusion, benchmark datasets, and agricultural decision-support systems. It also identifies gaps in field validation, interoperability, low-resource contexts, participatory governance, and trustworthy deployment. The review contributes a structured synthesis of how foundation models and multimodal AI may extend agricultural sensing, prediction, automation, and decision support. It highlights that technical capability alone is insufficient for responsible agricultural use. Robust data infrastructures, context-aware adaptation, transparent validation, and accountable governance will be central to whether these systems support sustainable and equitable agri-food transformation.

Digital agriculture technologies are increasingly positioned as tools for strengthening food security through improved sensing, prediction, automation, decision support, and resource management. These technologies are promoted as ways to help agricultural systems respond to climate stress, resource constraints, production volatility, and disruptions across agri-food value chains. Their potential relevance extends from farm-level monitoring to broader food-system coordination. Evidence on digital agriculture is distributed across multiple technology domains, including artificial intelligence, Internet of Things systems, robotics, remote sensing, and decision-support platforms. Food-security implications are often reported indirectly through productivity, efficiency, sustainability, resilience, risk reduction, or decision-quality outcomes. This dispersion makes it difficult to judge where the evidence is strongest and where food-security claims remain underdeveloped. This umbrella review synthesises review-level evidence on digital agriculture technologies in relation to food-security pathways. It examines how the reviewed literature connects digital agriculture with food availability, access, utilisation, stability, resilience, sustainability, productivity, and agri-food decision-making. The article focuses on review evidence rather than primary empirical findings. The approach compares review-level evidence across technology domains, outcome pathways, implementation conditions, and evidence gaps. It gives particular attention to whether digital agriculture evidence demonstrates direct food-security relevance or supports more indirect pathways through resource efficiency, monitoring capacity, operational timeliness, or improved decision support. It also distinguishes technical potential from demonstrated system-level benefit. The contribution of this umbrella review is to clarify where digital agriculture evidence is most developed, where food-security links remain indirect, and what future research is needed to evaluate digital agriculture as a contributor to resilient and inclusive food systems. The synthesis shows that digital agriculture should be assessed not only by technical performance but also by its capacity to support equitable, sustainable, and context-sensitive food-security outcomes.

Intelligent agriculture is increasingly shaped by automation, data platforms, decision-support tools, robotics, sensors, and algorithmic advisory systems. These technologies are promoted as ways to improve productivity, precision, efficiency, and sustainability in farming. Yet they also alter how agricultural decisions are made, who participates in those decisions, and whose knowledge is treated as authoritative. The central problem addressed in this article is that intelligent agriculture is often presented as a technical improvement while its implications for farmer agency remain contested. Farmer autonomy, judgement, dependency, skill, and voice can be strengthened by digital tools, but they can also be narrowed when systems become opaque, prescriptive, proprietary, or difficult to contest. This tension makes farmer agency a core question for responsible intelligent agriculture. This critical interpretive review examines how automation, platformization, and participatory design reshape farmer agency. It interprets agency not as an individual trait that farmers either possess or lose, but as a socio-technical capacity formed through relations among farmers, machines, platforms, data infrastructures, advisors, markets, and governance arrangements. The review therefore focuses on how agency is enabled, constrained, redistributed, and contested. The article examines key concepts, tensions, and assumptions in the reviewed literature. It does not report new primary empirical data, but critically synthesises existing scholarship to clarify how intelligent agriculture reorganises decision-making, knowledge, dependency, participation, power, and accountability. Particular attention is given to tensions between efficiency and autonomy, automation and situated judgement, platform convenience and dependency, data-driven advice and local knowledge, and participation and tokenism. The review argues that farmer agency is redistributed through socio-technical arrangements rather than simply preserved or removed by technology adoption. Responsible intelligent agriculture requires accountable automation, fair platform governance, participatory design, recognition of local knowledge, and institutional mechanisms that give farmers meaningful voice. Farmer agency should therefore be treated as a condition of technological legitimacy, not as an afterthought to innovation.

Artificial intelligence is increasingly positioned as a driver of agri-food transformation through sensing, prediction, optimisation, automation, and decision support. Across farms, supply chains, advisory systems, and governance settings, AI-enabled tools are changing how agricultural information is gathered, interpreted, and translated into action. These changes create important opportunities, but they also raise deeper theoretical questions about what kind of intelligence agri-food systems should develop. Much discussion of agri-food AI emphasises productivity, efficiency, technical accuracy, and optimisation. Yet sustainable agri-food transformation cannot be judged only by whether AI increases yields, lowers costs, or accelerates decisions. It must also be evaluated by whether intelligent systems strengthen resilience, support equity, and respect ecological limits. This article develops a theory of sustainable intelligence in agri-food systems. Sustainable intelligence is defined as the AI-enabled capacity of agri-food systems to sense, interpret, learn from, and respond to changing conditions while balancing productivity, resilience, equity, and ecological care. The theory treats these four dimensions not as separate goals, but as interdependent constructs that must be continuously aligned. The article builds theory by integrating scholarship on digital agriculture, food-system sustainability, resilience thinking, equity, ecological responsibility, and responsible AI. It focuses on the mechanisms through which sensing, prediction, optimisation, feedback, participation, and governance can either mediate or intensify tensions among agri-food system goals. In doing so, it shifts attention from AI as a technical instrument to AI as a socio-technical balancing capacity. The proposed theory explains sustainable intelligence as an emergent property of data integration, adaptive feedback, participatory governance, ecological sensitivity, and accountability. It argues that AI-enabled agri-food systems become sustainable not when they maximise a single performance metric, but when they help actors recognise trade-offs, negotiate priorities, and preserve the conditions for just and ecologically responsible production. Sustainable intelligence therefore offers a theoretical foundation for agri-food futures that are productive, resilient, equitable, and ecologically caring.

Artificial intelligence is increasingly promoted as a tool for improving agricultural productivity, food-system coordination, sustainability monitoring, risk prediction, quality control, and decision support. Across agri-food systems, AI is expected to support more timely decisions, more efficient resource use, and more adaptive responses to climatic, market, and production uncertainty. These expectations have encouraged investment in digital tools, analytics platforms, automated monitoring systems, and predictive applications. Yet the availability of AI tools does not mean that agri-food systems are prepared to use them effectively or responsibly. Readiness depends on the quality of data infrastructure, the strength of institutions, the capabilities of farmers and other users, and the maturity of governance arrangements. When these conditions are weak, AI implementation can deepen inequality, create dependence on opaque platforms, reduce trust, or generate risks that are difficult to identify and manage. This article develops an agri-food AI readiness framework that explains the conditions required for responsible and effective AI implementation. The framework conceptualises readiness as a socio-technical condition produced through the interaction of data infrastructure and digital capacity, institutional capacity and policy support, farmer capability and adoption readiness, and governance maturity and risk management. It emphasises that AI readiness is not a single technical threshold but a combined maturity condition across interconnected system dimensions. The framework contributes a structured way to assess whether agri-food systems are prepared to adopt, adapt, govern, and benefit from AI. It identifies how weaknesses in one dimension can reduce the effectiveness of the others, even where advanced technologies are available. By linking implementation capacity with governance responsibility, the framework supports more inclusive, trustworthy, and context-sensitive pathways for agri-food AI.

Artificial intelligence is increasingly embedded in agricultural decision support, farm platforms, automation, sensing, advisory systems, and data-driven management. These systems influence how farms interpret risk, allocate inputs, monitor environments, plan production, and respond to market and ecological uncertainty. As agricultural AI becomes more consequential, its governance must address not only technical performance but also the social conditions under which digital decisions are made, trusted, challenged, and revised. Agricultural AI governance is often framed around technical accuracy, transparency, regulatory compliance, or institutional oversight. While these are necessary, they are insufficient when farmers have limited influence over system design, data practices, model assumptions, recommendation logic, or dispute pathways. Governance weaknesses can produce exclusion, loss of autonomy, data misuse, algorithmic opacity, dependency on platforms, and digital decisions that affected users cannot meaningfully challenge. This article develops a participatory AI governance framework for agriculture centred on farmer voice, local knowledge integration, contestable digital decisions, and accountability safeguards. The framework treats governance as a socio-technical practice that connects design, deployment, monitoring, revision, and redress. It argues that responsible agricultural AI requires institutional mechanisms through which farmers and other affected actors can shape, interpret, evaluate, contest, and improve digital systems. The framework contributes to agricultural AI governance by showing how participation can strengthen legitimacy, trust, accountability, and contextual fit. It positions farmer voice as a condition of decision legitimacy, local knowledge as a condition of contextual accuracy, contestability as a condition of procedural fairness, and accountability safeguards as a condition of responsible institutional performance. Participatory governance is therefore presented as a practical foundation for agricultural AI that is more locally grounded, explainable, responsive, and accountable.

Smart agriculture increasingly combines artificial intelligence, sensor networks, automation, remote sensing, robotics, and decision-support systems to guide agricultural production and resource management. These technologies promise more precise monitoring, faster diagnosis, improved input targeting, and better coordination of farm operations. Their sustainability value, however, depends on how optimisation goals are defined and how decisions interact with ecological, technical, and social systems. A central problem is that algorithmic systems are often evaluated through yield gains, input efficiency, prediction accuracy, or operational performance. These metrics are important, but they do not necessarily reveal whether an improvement in one sustainability domain shifts pressure onto another. A farm system may become more productive while increasing water demand, energy dependence, biodiversity risk, or unequal exposure to technological burdens. This article develops a conceptual model of algorithmic sustainability trade-offs in smart agriculture. The model explains how yield, water, energy, biodiversity, and equity operate as connected outcome domains rather than isolated evaluation categories. It treats trade-offs as system-level tensions that emerge through optimisation objectives, data availability, decision rules, platform design, governance arrangements, and implementation contexts. The article contributes a systems-oriented framework for analysing when smart agriculture supports sustainability and when it redistributes environmental or social costs. It argues that algorithmic optimisation should not be treated as automatically sustainable simply because it improves efficiency or prediction. Instead, smart agriculture should be assessed by how its decisions balance productivity, resource use, ecological resilience, and distributional fairness.

Intelligent agricultural systems increasingly combine sensors, artificial intelligence, automation, robotics, remote sensing, decision support, digital platforms, and data infrastructures to support farm management. These systems promise more precise monitoring, faster decisions, and improved resource use. Yet their practical value depends on how they enter the social and ecological realities of farming. A persistent problem in intelligent agriculture is that systems are often evaluated through technical performance, productivity, efficiency, or predictive capability. Such evaluations can understate whether technologies fit farmer judgement, local work routines, ecological variability, and institutional conditions. When these dimensions are treated as secondary, intelligent systems may intensify rather than resolve farm-system tensions. This article develops a theory of the socio-technical farm based on human–technology–ecology alignment. The central argument is that intelligent agricultural systems become effective, responsible, and sustainable only when human actors, technological functions, and ecological processes are mutually coordinated. Alignment is therefore theorised as an active, situated, and adaptive relationship rather than as a static design feature. The theory-building synthesis integrates scholarship on intelligent agriculture, digital farming, socio-technical systems, human-centred design, ecological feedback, farm decision-making, and responsible innovation. It defines core constructs, explains alignment mechanisms, identifies misalignment risks, and specifies boundary conditions. The article positions farms as dynamic systems in which judgement, data, machinery, organisms, soil, weather, markets, and governance interact. The proposed theory contributes a farmer-centred and ecology-aware account of intelligent agriculture. It explains how adaptive feedback, contextual design, interpretability, advisory support, accountable governance, and ecological embeddedness shape whether intelligent systems strengthen or weaken farm performance. The theory redirects evaluation from automation alone toward aligned socio-technical design that supports human agency, technological responsibility, and ecological care.

Agri-food systems increasingly rely on analytics, artificial intelligence, machine learning, environmental monitoring, and decision-support tools to evaluate sustainability performance. These systems can estimate resource use, model environmental impacts, detect risks, and inform actions across farms, supply chains, markets, and governance settings. As analytics become embedded in sustainability decisions, their outputs increasingly shape how environmental problems are recognised and acted upon. Yet sustainability analytics does not become useful or responsible simply because it produces indicators, predictions, rankings, or dashboards. Environmental metrics may be technically sophisticated while remaining difficult for farmers, firms, auditors, regulators, and communities to interpret or challenge. This creates an explainability problem in which users need to understand not only what an analytic system reports, but also how the result was produced, what assumptions it contains, and what consequences may follow from acting on it. This article develops a conceptual framework for explainable sustainability analytics in agri-food systems. The framework treats explainability as a system-level requirement for linking environmental impact interpretation, risk explanation, responsibility attribution, human review, and accountability safeguards. It argues that sustainability analytics should be evaluated not only by predictive performance or measurement coverage, but also by whether it supports transparent, contestable, and context-sensitive decision-making. The article contributes a structured framework for interpreting environmental impact, explaining risk, attributing responsibility, and supporting accountable review in agri-food decision contexts. It positions explainable sustainability analytics as an approach that connects data, models, indicators, explanation mechanisms, human judgement, and governance safeguards. In doing so, it provides a foundation for sustainability analytics that is more interpretable, trustworthy, and actionable.

Intelligent agriculture increasingly incorporates automation, artificial intelligence, robotics, sensors, digital farm platforms, remote sensing, and predictive analytics into everyday farm management. These systems are frequently presented as tools for improving efficiency, precision, productivity, and labour allocation. Yet their value cannot be assessed only through technical performance or economic output. Their deeper significance lies in how they reshape the relationship between farmers, knowledge, technology, and agronomic decision-making. A central risk is that automation may substitute for farmer expertise rather than support it. When digital systems hide decision logic, prescribe action without explanation, or reduce farmers to passive operators, they can weaken practical skill, situated judgment, and opportunities for learning. Such effects are especially important in agriculture, where decisions are made under ecological variability, uncertainty, and local specificity. Intelligent systems therefore require evaluation not only as tools of control, but also as environments that can either preserve or erode human capability. This article develops a human-centred conceptual model for shifting from automation-oriented replacement toward augmentation-oriented support in agriculture. The model is organised around three connected capability domains: skill preservation, farmer judgment, and agronomic learning. It argues that intelligent agricultural systems become augmentative when they extend perception, improve interpretation, support deliberation, enable feedback, and preserve meaningful farmer control. The model also identifies design principles and boundary conditions that determine whether digital systems strengthen or weaken human capability. The article contributes a structured way of assessing intelligent farming beyond labour substitution, prediction accuracy, or platform efficiency. It proposes that digital agricultural systems should be judged by whether they help farmers notice more, reason better, decide more reflectively, and learn more effectively from farm outcomes. This framing positions augmentation as a human-centred design and governance challenge, not merely a technical feature. Agricultural intelligence is therefore understood as a shared capability between people, technologies, advisory systems, and situated agronomic practice.

Agri-food systems increasingly rely on artificial intelligence for farm decision support, automation, quality control, sustainability monitoring, logistics, supply-chain coordination, risk prediction, and resource management. These applications promise productivity gains and improved responsiveness to environmental and market pressures. Yet their growing influence also creates new governance demands because AI systems can shape decisions that affect farms, firms, workers, consumers, ecosystems, and public institutions. Trustworthy AI in agri-food systems cannot be established through technical performance claims alone. A model may be accurate in a narrow test environment while remaining opaque, unsafe under changing field conditions, unfair across producer groups, or unable to substantiate sustainability claims. Certification therefore needs to move beyond reputational signalling and provide credible assurance that AI systems can be examined, challenged, monitored, and held accountable. This article develops a trustworthy AI certification framework for agri-food systems centred on auditability, safety, fairness, and sustainability-claim verification. The framework treats certification as a governance mechanism rather than a simple label or checklist. It identifies the evidence, assurance procedures, monitoring expectations, and institutional safeguards needed to make certification credible in technically complex and socially consequential agri-food settings. The article contributes a structured approach for regulators, certification bodies, technology providers, agri-food firms, farmer organisations, sustainability auditors, and public institutions. It argues that trustworthy AI certification should depend on verifiable evidence, independent auditability, lifecycle risk assurance, distributional accountability, environmental claim verification, and clear responsibilities across the AI value chain. Certification becomes meaningful only when it supports systems that are auditable, safe, fair, environmentally credible, and publicly accountable.

Food security early warning systems increasingly draw on artificial intelligence, remote sensing, predictive analytics, climate information, crop monitoring, market indicators, and vulnerability data to identify emerging risks before they become severe crises. These systems are expected to provide timely signals about threats to food availability, access, utilisation, and stability across diverse ecological, economic, and social settings. Yet many early warning approaches remain fragmented. Climate risk, market instability, crop stress, and human vulnerability are often analysed in separate technical workflows, which can weaken interpretation, delay action, or produce alerts that are insufficiently linked to response capacity. This article develops a conceptual architecture for AI-enabled food security early warning systems. The architecture treats early warning as a layered process that connects data ingestion, risk-domain analytics, signal integration, uncertainty interpretation, alert generation, decision support, response targeting, and feedback governance. The proposed architecture explains how multi-source data and predictive models can be translated into interpretable and actionable food security warnings. It also emphasises that AI-enabled early warning can fail when data are incomplete, model outputs are opaque, vulnerable populations are underrepresented, or alerts are disconnected from institutions capable of acting.

Intelligent agriculture increasingly relies on data-intensive systems, including sensors, connectivity, decision-support tools, automation, platforms, machine learning, and analytics. These systems are often presented as pathways toward more productive, efficient, resilient, and sustainable farming. Yet their social effects depend on how access, control, capability, risk, and value are distributed across agricultural actors. The central problem addressed in this article is that intelligent agriculture can deepen inequality when digital systems are unevenly organised. Farmers and rural actors may face different levels of access to infrastructure, data, technical support, platform services, financial capacity, and bargaining power. As a result, the benefits of digitalisation may accumulate among actors already positioned to use, govern, and monetise data-driven systems. This article develops a theory of digital inequality in intelligent agriculture. The theory explains how data access, infrastructure gaps, platform dependence, and uneven value capture interact to shape distributional outcomes. It treats inequality not as a side effect of incomplete adoption but as a process produced through socio-technical relations. The approach integrates scholarship on digital agriculture, rural digital divides, agricultural platforms, farmer capability, data governance, infrastructure inequality, responsible innovation, and value distribution. Rather than treating smart farming as a neutral technological transition, the article develops constructs, mechanisms, relationships, boundary conditions, and propositions. The aim is to explain how digital agricultural systems can reproduce, intensify, or reduce inequality.The proposed theory argues that digital inequality in intelligent agriculture emerges through linked pathways of data exclusion, infrastructure disadvantage, capability constraint, platform control, and value appropriation. These pathways influence who can participate in intelligent agriculture, who becomes dependent on digital intermediaries, who bears risks, and who captures benefits. The article concludes that intelligent agriculture should be evaluated by its distributional consequences as well as its technical performance.

Intelligent agriculture increasingly uses artificial intelligence, sensors, remote sensing, automation, modelling, and decision-support systems to monitor and manage agricultural production. These tools can strengthen observation, prediction, and coordination across farms and landscapes. Yet their contribution to agricultural transformation depends on the ecological aims toward which they are designed and governed. Regenerative agriculture requires more than improved efficiency or narrower optimisation of inputs. It calls for soil restoration, biodiversity protection, ecological feedback, and long-term system health. Intelligent systems therefore need to be evaluated not only by productivity gains, but also by their capacity to support ecological recovery. This article develops a conceptual framework for regenerative intelligence in agriculture. Regenerative intelligence is defined as the AI-supported capacity of agricultural systems to monitor, interpret, and support soil restoration, biodiversity protection, and long-term system health through adaptive and ecologically accountable decision pathways. The framework explains how sensing, ecological analytics, modelling, decision support, monitoring, feedback, and governance can contribute to regenerative agricultural transformation. It also clarifies that AI-supported agriculture is not automatically regenerative. Its regenerative value depends on ecological indicators, farmer participation, responsible governance, biodiversity sensitivity, soil-health interpretation, and sustained learning from ecological outcomes.

Intelligent agriculture increasingly relies on sensors, remote sensing, machine learning, automation, digital platforms, and decision-support systems to produce rapid agricultural insight. These systems promise earlier detection, faster response, and more precise management across crop, soil, water, livestock, and farm-business decisions. Their appeal is closely tied to the speed, volume, and apparent objectivity of agricultural data streams. Yet agricultural intelligence cannot be reduced to fast data alone. Farms are living, seasonal, historically shaped, and locally interpreted systems in which the meaning of data depends on field context, management history, ecological feedback, and farmer judgement. Rapid signals can be useful, but they can also encourage premature conclusions when detached from temporal and local conditions. This perspective argues that intelligent agriculture needs slow data to remain contextually valid, seasonally sensitive, locally meaningful, and responsibly updated. Slow data is defined here as context-rich, temporally grounded, locally interpreted, ecologically informed, and responsibly revised data that strengthens agricultural AI by preventing overfitting, decontextualised automation, and unexamined model drift. It complements rather than rejects fast sensing and predictive analytics. The article develops a slow data perspective for intelligent agriculture by connecting context, seasonality, local knowledge, farmer feedback, model revision, and governance safeguards. It argues that agricultural AI becomes more responsible when it slows down interpretation enough to recognise uncertainty, ecological variability, and the limits of model transfer. The contribution is a perspective on agricultural intelligence that is faster when needed, but patient enough to remain agronomically meaningful.

Agri-food systems increasingly operate under compound crises in which climate shocks, supply disruptions, market instability, ecological stress, and social vulnerability interact. These crises do not affect production, distribution, livelihoods, and food access in isolation. They move through connected risk pathways that can destabilise farms, supply chains, public institutions, and households at the same time. Artificial intelligence can support prediction, monitoring, early warning, and decision support in these contexts. However, technical forecasting alone does not constitute resilience when risks cascade across multiple domains. Agri-food resilience requires that predictive information be translated into adaptive decisions, coordinated response, and food security protection. This article develops a conceptual model of AI-supported agri-food resilience under compound crises. The model links climate shocks, supply disruptions, farmer adaptation, and food security governance as interacting domains. It treats AI as a decision-support function embedded within social, institutional, ecological, and market systems rather than as a stand-alone technological solution. The model identifies resilience mechanisms that connect risk detection, signal integration, uncertainty communication, adaptive decision support, institutional coordination, feedback learning, and accountability. It also clarifies boundary conditions that determine whether AI strengthens or weakens resilience. These include data quality, interpretability, local relevance, farmer participation, institutional response capacity, equity, and governance accountability. The article contributes a structured conceptual model for understanding how AI can support agri-food resilience when analytics are aligned with adaptation needs and food security objectives. It argues that resilient agri-food AI systems must connect prediction with human judgement, local knowledge, public action, and accountable coordination. The proposed model offers a basis for future empirical testing, policy design, and responsible innovation in crisis-sensitive food systems.

Foundation models are increasingly investigated as reusable architecture-level systems for agricultural sensing, remote-sensing interpretation, multimodal learning, knowledge access, phenotyping, monitoring, and decision-support assistance. However, the evidence remains dispersed across model families, data modalities, agricultural applications, and validation settings. This systematic review synthesises the technical architectures, data requirements, opportunities, risks, deployment challenges, evidence gaps, and research priorities associated with foundation models in smart farming and precision agriculture. Peer-reviewed journal literature was identified through structured database and publisher-platform searches, supplementary reference checking, predefined eligibility criteria, sequential screening, claim-specific evidence extraction, and thematic synthesis. Evidence was organised across language, vision, vision-language, multimodal, geospatial, remote-sensing, time-series, self-supervised, transfer-learning, and domain-adaptation approaches. The reviewed literature indicates that foundation-model approaches may support transferable representation learning, reduced annotation dependence, multimodal data integration, crop and livestock monitoring, remote-sensing analysis, plant phenotyping, agricultural knowledge services, and bounded decision-support workflows. Nevertheless, reported results are commonly task-, dataset-, region-, sensor-, or application-specific. Important limitations include agricultural data scarcity and heterogeneity, geographic and temporal domain shift, uncertain generalisation, bias, weak explainability, inadequate uncertainty communication, privacy and cybersecurity risks, unclear accountability, computing demands, and limited farmer-centred or operational evaluation. Priorities include agricultural benchmark suites, domain-specific pretraining resources, multimodal and spatiotemporal adaptation, low-resource representation, robustness testing, calibrated uncertainty, human-in-the-loop evaluation, privacy-preserving development, sustainable-computing assessment, and lifecycle governance. Foundation models represent a promising research direction, but benchmark performance or pretraining scale alone does not establish agronomic validity, operational safety, farmer trust, or deployment readiness.

Generative artificial intelligence is emerging as a potentially important component of precision agriculture through its capacity to create synthetic data, generate agricultural imagery, retrieve and reformulate knowledge, support conversational advisory interfaces, and assist decision-support workflows. However, evidence of technical feasibility is developing faster than evidence of agronomic validity, reliability, farmer-centred usefulness, and responsible deployment. This critical review evaluates generative AI as a distinct agricultural technology class, with particular attention to generative adversarial networks, diffusion models, variational autoencoders, large language models, vision-language models, foundation models, fine-tuning, prompting, domain adaptation, and retrieval-augmented generation. It critically examines the proposed use of synthetic crop, disease, weed, pest, and remote-sensing data for augmentation, class-imbalance mitigation, and label-scarcity support, while emphasizing image realism, biological plausibility, source-data quality, and synthetic-real domain gaps. The review also assesses generative decision support and advisory systems, distinguishing accessible language generation and knowledge retrieval from agronomically validated recommendations. Reliability concerns include hallucination, factual inaccuracy, uncertainty underreporting, poor local adaptation, robustness limitations, benchmark insufficiency, and failures of human oversight. Responsible deployment further requires data governance, privacy protection, accountability, transparency, explainability, farmer trust, digital inclusion, risk monitoring, and assessment of computational and sustainability costs. The review contributes an integrated opportunity-risk interpretation and identifies priorities for domain-specific benchmarks, synthetic-data validation, hallucination measurement, uncertainty-aware assistance, human-in-the-loop evaluation, farmer-centred design, governance documentation, and cautious field-readiness assessment.

Remote sensing and machine learning have become prominent components of smart farming research, generating an expanding body of review literature across agricultural monitoring and prediction applications. This umbrella review synthesises review-level evidence concerning crop monitoring, disease detection, yield forecasting, and decision support while examining the methodological conditions that shape evidence strength and transferability. Peer-reviewed systematic reviews, scoping reviews, structured reviews, surveys, meta-reviews, and other eligible review-level articles were identified through searches of major academic databases and publisher platforms. Evidence was organised by application domain, sensing modality, analytical method, validation design, generalisation, uncertainty, reproducibility, and decision-support relevance. Crop-monitoring reviews describe extensive use of satellite, unmanned aerial vehicle, multispectral, hyperspectral, thermal, and multimodal data for crop mapping, growth assessment, phenotyping, stress monitoring, and related applications. Disease-detection reviews report rapid expansion of image-based machine learning and deep learning, but repeatedly identify data scarcity, class imbalance, curated-image dependence, and limited cross-context validation. Yield-forecasting reviews identify recurring integration of remote sensing, weather, soil, crop-model, and management information, although forecast horizons, spatial and temporal validation, uncertainty communication, and decision relevance remain inconsistent. Across domains, reported technical promise is moderated by heterogeneous datasets, benchmark inconsistency, review overlap, incomplete reproducibility, and limited evidence of transfer across crops, regions, seasons, and acquisition conditions. Review-level evidence supports a substantial research opportunity for remote sensing and machine learning in smart farming, but it does not independently establish field readiness, diagnostic reliability, operational forecasting performance, or deployment readiness. Stronger review reporting, external validation, transparent benchmarking, uncertainty assessment, reproducible workflows, and responsible decision-support evaluation are required.

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.

Smart farming technologies are often presented as innovations whose adoption depends mainly on technical availability, economic advantage, or farmer willingness. Such interpretations do not adequately explain why similar technologies are adopted and sustained in some settings but resisted, selectively used, discontinued, or abandoned in others. This realist review develops an explanatory synthesis of smart farming adoption through context–mechanism–outcome reasoning. It examines how farm structure, farmer capability, financial resources, infrastructure, connectivity, advisory support, institutional conditions, social legitimacy, platform relations, and data governance influence farmers’ interpretations of technological opportunities and risks. Adoption is distinguished from technology acceptance, partial adoption, sustained use, discontinuance, abandonment, and implementation failure. Enabling mechanisms include perceived usefulness, compatibility, trust, decision confidence, credible value, and experiential learning. Constraining mechanisms include affordability concern, return uncertainty, complexity, privacy concern, autonomy concern, platform dependency, and maintenance burden. The synthesis indicates that no contextual factor or mechanism independently guarantees adoption or implementation success. Similar technologies may generate different outcomes because farmers encounter different resources, constraints, relationships, and implementation experiences. The review proposes a recursive programme theory in which appraisal, trial, implementation experience, feedback, and changing farm conditions continually reshape perceived value, trust, capability, and willingness to continue. Responsible implementation therefore requires context-sensitive evaluation, farmer-centered usability assessment, reliable infrastructure, credible advisory and technical support, interoperability, transparent data governance, and attention to exclusion. The proposed configurations support explanatory synthesis and empirical testing rather than universal prediction or implementation prescription.

Foundation models are becoming relevant to precision agriculture because they can connect sensing, remote sensing interpretation, multimodal data integration, knowledge retrieval, advisory communication, and farm decision-support workflows. Their broad capability, however, does not establish agronomic validity, trustworthy performance, operational safety, or deployment readiness. This article develops an original governance and technical safeguard framework for responsible agricultural foundation models. The framework treats these models as potential components of decision infrastructure and integrates trustworthy artificial intelligence requirements with data governance, validation, human oversight, farmer participation, monitoring, and explicit claim boundaries. It identifies requirements concerning data quality, provenance, documentation, transparency, explainability, interpretability, uncertainty communication, robustness, generalization, bias, fairness, privacy, cybersecurity, accountability, auditability, and traceability. It also maps risks arising from opaque pretraining data, uncertain data control, hallucination, factual error, agronomic invalidity, platform dependency, model drift, data drift, and unclear responsibility. Technical safeguards include data screening, domain-adaptation assessment, external and local validation, robustness testing, bias and fairness review, factual and agronomic checking, human-in-the-loop review, and continuing monitoring. Responsible deployment principles add purpose limitation, context sensitivity, incident response, withdrawal planning, and non-autonomy boundaries. The contribution is a structured conceptual framework that connects governance questions with technical evidence and accountable actors without claiming legal compliance, guaranteed trustworthiness, farmer approval, or deployment readiness.

Generative artificial intelligence is creating new possibilities for conversational access to agricultural information, farm data, and decision-support resources. However, fluent interaction does not establish factual accuracy, agronomic validity, farmer trust, or operational safety. This article proposes an original conceptual architecture for generative AI-enabled farm advisory systems in precision agriculture. The architecture treats conversational decision support as a governed socio-technical process rather than a standalone chatbot function. It connects farm advisory use-case definition, farmer query interpretation, heterogeneous agricultural data inputs, curated knowledge retrieval, generative model layers, response grounding, advisory-output controls, uncertainty communication, human oversight, expert escalation, farmer feedback, monitoring, and controlled system updating. Sensor observations, remote-sensing products, weather information, soil and crop data, farm-management records, and textual agricultural knowledge are positioned behind data-quality, provenance, documentation, and access-control requirements. Foundation models, large language models, vision-language models, multimodal models, prompting, domain adaptation, and retrieval-augmented generation are treated as optional architectural components whose appropriateness depends on the bounded advisory task. The architecture further distinguishes factual checking from agronomic validity review and separates farmer-facing explanations from advisor-facing evidence assessment. Safety and trust requirements include risk classification, hallucination monitoring, privacy, cybersecurity, auditability, farmer autonomy, explicit advisory boundaries, and escalation pathways. The principal contribution is a structured framework for designing and evaluating conversational farm advisory systems without implying autonomous decision authority, validated agronomic advice, guaranteed trust, operational safety, or deployment readiness.

Generative artificial intelligence is expanding the conversational and interpretive functions available within precision-farming decision support, yet fluent responses may contain factual inaccuracies, unsupported claims, fabricated evidence, agronomically invalid interpretations, local-context mismatches, or understated uncertainty. This article develops a conceptual artificial-intelligence safety framework for managing these risks in generative agricultural decision-support systems. The framework treats hallucination risk as a socio-technical safety problem rather than solely as a language-model defect. It links agricultural use-case definition, advisory-output classification, farm-level consequence assessment, data and knowledge provenance, retrieval and response grounding, hallucination screening, factual verification, agronomic-validity review, uncertainty communication, human oversight, expert escalation, farmer feedback, traceability, accountability, monitoring, and controlled updating. Generative outputs are differentiated according to whether they provide information, interpretation, scenario explanation, or recommendation-like content, because the evidentiary and review requirements increase with potential consequence. Validation is similarly separated into factual checking, external evaluation, local-context assessment, agronomic review, and future field-validation planning. The framework establishes decision boundaries against unsupported agronomic advice, autonomous farm action, hallucination-elimination claims, safety guarantees, and deployment-readiness claims. It also allocates responsibilities among developers, platform providers, data stewards, knowledge-base curators, agricultural reviewers, and farmers while preserving farmer autonomy. The main contribution is an integrated safety model for structuring how hallucination-prone generative outputs may be interpreted, reviewed, restricted, escalated, recorded, and updated within precision-farming decision support. The framework is conceptual and does not independently establish advisory correctness, operational safety, legal compliance, or deployment readiness.

Climate-smart precision agriculture is increasingly discussed as a means of connecting farm data, environmental observation, and analytical modeling with climate adaptation, mitigation-relevant assessment, and resilience planning. However, data availability and predictive capability alone do not establish climate benefit, decision reliability, or farm readiness. This article proposes a conceptual data-science framework that organizes climate-smart use-case definition, farm-context mapping, multi-source data integration, adaptation analytics, mitigation-relevant analysis, climate-risk modeling, uncertainty assessment, resilience-oriented decision support, sustainability feedback, monitoring, and governance safeguards. The framework distinguishes climate hazards, exposure, vulnerability, adaptive capacity, forecasting, scenario analysis, and operational decision support while preserving boundaries between analytical indicators and verified outcomes. Remote sensing, sensors, weather and climate records, soil and crop observations, water and resource-use data, machine learning, geospatial analysis, time-series modeling, and data fusion are positioned as conditional inputs and methods rather than automatic sources of climate benefit. The framework also requires local validation, uncertainty communication, farmer participation, human oversight, data-quality assessment, provenance, interoperability, and feedback updating. Its main contribution is an integrated architecture for interpreting how data-science functions may support climate-smart precision agriculture without presenting modeled outputs as guaranteed adaptation, verified mitigation, proven resilience, carbon accounting, or deployment readiness. The article therefore frames climate-smart precision agriculture as a governed learning system whose value depends on evidence quality, contextual interpretation, transparent limitations, and iterative assessment.

Artificial intelligence is increasingly embedded in smart farming systems, yet explanation requirements are often treated as a single technical attribute rather than as stakeholder-dependent needs. This article develops a conceptual taxonomy of explainability requirements across agricultural decision contexts. The taxonomy approach distinguishes stakeholders by their roles, decision consequences, explanation purposes, required explanation objects, usability expectations, accountability needs, trust risks, and implementation boundaries. Farmers may require accessible, locally relevant, actionable, and uncertainty-aware explanations that preserve autonomy. Agronomists and advisors may require model logic, input relevance, data-quality information, output rationale, limitations, and evidence supporting professional validation. Regulatory stakeholders may require traceability, documentation, auditability, risk justification, and clear responsibility allocation. Platform providers, technology providers, data intermediaries, system operators, and data stewards may require lifecycle transparency, monitoring information, provenance records, version histories, and incident reconstruction. Supply-chain and sustainability actors may require interpretable provenance and decision boundaries without treating explanation as proof of quality, sustainability, or compliance. The taxonomy also identifies failures including under-explanation, over-explanation, misleading explanations, uncertainty omission, explanation overload, false confidence, provenance opacity, accountability gaps, and trust miscalibration. Its main contribution is a structured account of how explanation requirements change across stakeholders and decision settings. The taxonomy is conceptual and requires stakeholder validation, usability testing, governance assessment, and domain-specific evaluation before operational application.

Autonomous farming is emerging as a precision agriculture frontier in which sensing, environmental awareness, reasoning, decision logic, robotic action, and adaptive feedback are linked within increasingly integrated technical systems. Yet autonomy in agriculture cannot be reduced to unattended machine operation because field environments are variable, data quality is uneven, physical actions are consequential, and farmers and operators retain essential contextual and supervisory roles. This article develops an original conceptual model for analysing autonomous farming systems as supervised socio-technical architectures rather than self-sufficient farm managers. The model is constructed through conceptual synthesis of agricultural robotics, computer vision, machine learning, navigation, control, human–robot interaction, and responsible innovation scholarship. It organizes autonomy into connected layers: use-case definition; sensing and environmental awareness; data quality and fusion; reasoning, planning, and uncertainty interpretation; acting, control, and robotic execution; learning, feedback, and drift monitoring; and human oversight, safety, accountability, and governance. The main contribution is a closed-loop account of how farm observations may become bounded robotic actions only when perception is validated, reasoning is contextually interpreted, plans remain within operating constraints, execution is monitored, and human intervention remains available. Learning is treated as governed model updating rather than automatic self-improvement. The model therefore clarifies both enabling relationships and non-guarantee boundaries: conceptual integration alone does not establish safe autonomous operation, productivity improvement, legal sufficiency, or deployment readiness. It offers a structured basis for future research, local validation planning, supervisory design, and responsible assessment of autonomous farming within precision agriculture.

Artificial intelligence is becoming embedded in precision agriculture through data-intensive monitoring, predictive models, digital platforms, advisory systems, robotics, and automated machinery. These arrangements complicate accountability because consequential outputs may result from interactions among data providers, model developers, platform operators, advisors, machinery manufacturers, system integrators, service providers, and farm operators rather than from a single actor or technical component. This article proposes a conceptual accountability framework for examining AI liability issues across agricultural AI systems without treating accountability mapping as a substitute for legal interpretation. The framework begins with agricultural use-case definition and stakeholder-role mapping, then examines data-level duties concerning quality, provenance, access, control, privacy, cybersecurity, and governance. It further maps model-level responsibilities for documentation, validation, robustness, bias assessment, explainability, uncertainty communication, and drift monitoring. Platform and advisory layers are addressed through governance, interface design, monitoring, escalation, human review, professional interpretation, and farmer-autonomy requirements. Machinery, automation, and operational risks are incorporated through control-system responsibilities, sensor-to-actuator traceability, supervisory control, fail-safe design, maintenance, incident reporting, and operational boundaries. The framework also identifies liability gaps created by fragmented evidence, unclear validation ownership, platform ambiguity, ineffective human oversight, and incomplete traceability. Its contribution is a structured method for clarifying responsibility pathways, evidence needs, decision boundaries, and governance gaps across interconnected agricultural AI layers. The framework is conceptual and does not determine legal liability, establish compliance, certify safety, allocate blame, or demonstrate deployment readiness.