Search

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

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

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

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

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

Digital twins are increasingly proposed as cyber-physical representations that may support precision agriculture through monitoring, simulation, prediction, optimization, and decision support. However, agricultural systems described as digital twins vary substantially in data quality, interoperability, synchronization, modelling depth, predictive reliability, decision functionality, validation, and operational governance. This article proposes a conceptual maturity framework that evaluates agricultural digital twins through capability progression rather than technology adoption alone. The framework begins with descriptive monitoring and advances through real-time farm representation, simulation and scenario analysis, prediction and optimization, and supervised autonomous decision support. Monitoring maturity depends on reliable sensing, remote sensing, Internet of Things integration, data fusion, spatial and temporal adequacy, traceability, and human-interpretable representation. Simulation and prediction maturity require explicit farm-system boundaries, calibrated models, uncertainty estimation, local validation, and evidence that analytical outputs remain valid for their intended context. Optimization maturity additionally requires transparent objectives, constraints, robustness assessment, and distinction between predictive accuracy and decision value. The highest proposed level permits only narrowly bounded and supervised decision initiation supported by human authority, explainability, safety mechanisms, governance, cybersecurity where relevant, and continuous feedback. The framework does not constitute a validated maturity standard or establish agronomic, environmental, economic, or autonomous decision benefits. Its principal contribution is an integrated structure for assessing capability readiness, identifying progression barriers, and preventing monitoring, simulation, prediction, or optimization functions from being overinterpreted as evidence of deployment-ready autonomy.

Artificial intelligence is increasingly embedded in precision agriculture through sensing, remote observation, predictive modelling, automated classification, and decision-support applications. Technical performance alone, however, is insufficient for determining whether these systems are responsibly designed or appropriately used. This article proposes an original governance framework that connects agricultural context definition, farm-data governance, transparency, explainability, accountability, sustainability assessment, farmer participation, human oversight, risk safeguards, implementation-readiness review, monitoring, and adaptive feedback. The framework treats transparency as a requirement for documenting data provenance, data quality, model assumptions, system limitations, and decision pathways, while explainability is positioned as a task-specific means of supporting interpretation rather than proof of model validity. Accountability is structured through explicit allocation of responsibilities among developers, platform providers, data stewards, advisors, farmers, and oversight actors. Environmental, social, and economic sustainability are assessed alongside digital inclusion, usability, local relevance, farmer autonomy, and trust-building conditions. The framework also addresses algorithmic opacity, bias, model drift, poor local calibration, data misuse, cybersecurity, automation dependence, accountability gaps, and implementation overclaiming. Its principal contribution is a layered, adaptive governance model for responsible agricultural decision support. The framework supports governance planning and research design but does not establish empirical validity, legal compliance, policy acceptance, farmer approval, sustainability performance, trust, certification, or deployment readiness.

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.

Decision-support systems are increasingly positioned as mechanisms for translating agricultural data and analytical models into farm-management recommendations, yet their methodological quality cannot be judged through predictive performance alone. This article proposes an original methodological framework for designing and evaluating decision-support systems in precision agriculture. The framework integrates farm-management decision-context definition, stakeholder mapping, data-quality and provenance assessment, model and recommendation-logic documentation, explainability and interpretability requirements, usability and farmer-centered design, validation planning, decision-reliability assessment, adoption-barrier analysis, implementation-condition assessment, human oversight, and iterative feedback. Explainability is treated as a role-sensitive design requirement involving input visibility, output explanation, decision rationale, uncertainty communication, traceability, and appropriate explanation timing and format. Usability is addressed through farmer participation, co-design, workflow integration, accessibility, local relevance, manageable decision-support workload, training, and feedback mechanisms. Validation is extended beyond internal model testing to include external and local validation, calibration, error analysis, field-validation planning, drift monitoring, and review of the complete recommendation pathway. Adoption is interpreted as a contextual and voluntary process shaped by perceived usefulness, trust, affordability, infrastructure, digital capability, interoperability, support, and governance. Human oversight remains necessary because neither technical performance nor explanation quality independently establishes decision reliability. The framework contributes a structured method for coordinating design and evaluation activities while retaining explicit boundaries against claims of guaranteed decision quality, adoption, or deployment readiness.

Agricultural data markets are becoming integral to precision farming as data generated by sensors, machinery, remote-sensing systems, farm-management platforms, advisory services, supply chains, and public information infrastructures acquire operational and economic value. However, the expansion of data exchange creates policy challenges that cannot be assessed through innovation, productivity, or monetization potential alone. This article develops an original policy framework that connects agricultural data-market boundaries, actors, data flows, ownership claims, access arrangements, effective control, farmer participation, privacy, competition, platform power, interoperability, fair exchange, accountability, and implementation safeguards. The framework distinguishes data ownership from access, control, portability, consent, and practical farmer agency. It also treats privacy protection as one component of a wider governance architecture that must address platform dependency, market concentration, switching costs, asymmetric information, bargaining-power imbalances, data-intermediary influence, and unclear accountability. Interoperability is conceptualized as a combination of technical, semantic, organizational, and governance conditions rather than as a sufficient guarantee of fair exchange. The framework further identifies policy gaps involving ambiguous rights, fragmented portability, weak consent, opaque value capture, deficient dispute pathways, and limited monitoring capacity. Its principal contribution is a structured policy-analysis model that links risk identification to context-sensitive safeguards, stakeholder feedback, monitoring, and framework updating. The framework supports policy-oriented analysis but does not independently establish legal compliance, guarantee farmer protection, certify market fairness, or demonstrate implementation readiness.

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.

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 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 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.

Smart farming platforms are frequently conceived around persistent connectivity, modern devices, scalable cloud services, specialized technical support, and the financial capacity to adopt integrated digital systems. These assumptions may not hold in smallholder and connectivity-constrained agricultural settings, where infrastructure gaps, uneven digital skills, limited advisory access, maintenance difficulties, data costs, language diversity, and uncertain platform governance shape whether digital services are usable or sustainable. This article develops a conceptual design science framework for low-resource smart farming platforms. The framework links problem relevance, contextual requirement definition, design principles, platform artifact logic, implementation conditions, evaluation needs, feedback, and framework updating. It focuses on affordability, transparent costs, incremental adoption, modular architecture, interoperability, offline-first operation, low-bandwidth communication, local usability, accessible interaction, advisory integration, maintenance capacity, farmer data control, and trust. The proposed approach treats smallholders as heterogeneous users operating within diverse production systems, infrastructures, institutions, and knowledge networks. Platform functions are therefore organized as configurable responses to identified conditions rather than as a universal technology package. The principal contribution is a structured design science logic for translating low-resource agricultural problems into bounded platform requirements and evaluable conceptual artifacts. The framework can guide platform design and research planning, but it does not independently demonstrate affordability, inclusion, adoption, scalability, sustainability, operational safety, or deployment readiness. These outcomes require local usability assessment, affordability analysis, governance review, maintenance evaluation, implementation research, and field validation involving intended users and supporting institutions.

Regenerative agriculture is increasingly associated with soil improvement, ecological restoration, climate adaptation, and more resilient farming systems, yet these outcomes cannot be inferred from a management label or from digital measurement alone. This article develops a conceptual framework for data-driven regenerative agriculture that links precision farming, soil health monitoring, biodiversity monitoring, ecological feedback, climate-risk interpretation, farmer participation, and responsible decision support. The framework approach treats regeneration as a context-dependent monitoring and interpretation problem involving heterogeneous data, multiple spatial and temporal scales, uncertain ecological relationships, and explicit claim boundaries. Precision farming contributes spatially and temporally resolved observations from sensors, remote sensing, soil sampling, crop monitoring, water data, and farm-management records, while soil-health interpretation requires function-linked indicators, longitudinal baselines, local validation, and separation of monitoring from verified restoration or carbon-accounting claims. Biodiversity monitoring is organized around agrobiodiversity, functional biodiversity, habitat conditions, landscape context, ecological indicators, and field-validated observation methods. Climate resilience is framed through hazards, exposure, vulnerability, adaptive capacity, farm-system adaptation, and uncertainty rather than as a guaranteed outcome. Regenerative decision support integrates these domains through data-quality review, provenance, interoperability, ecological interpretation, farmer knowledge, human oversight, sustainability feedback, and iterative framework updating. The principal contribution is a structured conceptual architecture for connecting digital agricultural capabilities with cautious ecological reasoning. The framework supports research design and monitoring-system development but does not independently verify regenerative outcomes, environmental performance, biodiversity recovery, soil restoration, carbon sequestration, climate resilience, operational safety, or deployment readiness.

Agricultural artificial intelligence is commonly compared through benchmark datasets and technical metrics before any field experimentation occurs. Such comparisons can help identify promising models, reveal methodological weaknesses, and organize evidence for further testing, but they cannot establish field readiness, agronomic validity, operational safety, or deployment suitability. This article proposes a conceptual framework for benchmarking agricultural AI without field experiments. The framework separates five linked assessment domains: model readiness, technical validation, data quality, contextual fit, and deployment risk and decision validity. It addresses benchmark-task clarity, metric justification, dataset representativeness, provenance, label and ground-truth uncertainty, structured validation, domain shift, robustness, calibration, uncertainty, error patterns, bias, explainability, human oversight, and monitoring requirements. Non-empirical evaluation is treated as a structured screening process based on documentation, reported evidence, cross-context reasoning, and explicit claim boundaries. The framework produces a conditional benchmark-readiness statement and a field-validation gap register rather than a deployment decision. Its principal contribution is to distinguish benchmark performance from decision validity and deployment readiness while giving researchers, developers, evaluators, and agricultural stakeholders a transparent structure for comparing evidence before field testing. The framework is conceptual, does not replace field experiments, and should be updated as new data, validation evidence, user feedback, and operational observations become available.

Agricultural artificial intelligence increasingly combines sensor measurements, remote sensing, farm records, predictive modelling, and digital decision support. Yet agricultural context cannot be adequately represented through machine-readable data or model performance alone. Farming decisions are also shaped by tacit knowledge, farmer expertise, local ecological understanding, seasonal experience, farm history, cultural relationships, and institutional power. This article proposes a conceptual framework for integrating these dimensions into context-aware agricultural AI without treating local or Indigenous knowledge as extractable technical inputs. The framework begins with context-aware use-case definition and farm, agroecological, cultural, and historical context mapping. It then distinguishes tacit, experiential, situated, local, Indigenous, traditional ecological, scientific, and model-based knowledge while preserving differences in authority, representation, and validation. Participatory design, farmer-centered design, knowledge co-production, human oversight, local validation, knowledge sovereignty, Indigenous data sovereignty, access control, and ethical review are positioned as interconnected requirements rather than optional additions. The framework also addresses knowledge extraction, misappropriation, decontextualization, translation loss, contextual bias, algorithmic opacity, consent ambiguity, unequal benefit distribution, and trust overclaiming. Its principal contribution is a staged architecture that links knowledge plurality with contextual interpretation, governance safeguards, uncertainty communication, feedback, and explicit decision boundaries. The framework is intended to guide conceptual development and research design. It does not authorize knowledge use, establish community or Indigenous approval, validate Indigenous knowledge through AI, guarantee cultural appropriateness or trust, or demonstrate operational or deployment readiness.