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Obtaining body mass in large-scale rearing and production environments is difficult, making non-contact assessment methods indispensable. This paper introduces an original pipeline for predicting pig body mass based on an adapted mask region convolutional neural network (mask R-CNN). The adaptation deploys ResNeSt as the backbone feature-extraction architecture, aiming to bolster the capacity to derive features from imagery. A feature pyramid network (FPN) was appended to this backbone to enable feature merging across multiple scales. Within the region proposal network (RPN), a channel attention mechanism (CAM) was installed alongside a spatial attention mechanism (SAM), enabling the dynamic combination of localized characteristics with their long-range interdependencies. This configuration captures holistic contextual knowledge, ultimately improving segmentation fidelity. The adapted architecture achieved precision (P) of 90.33%, recall (R) of 89.85%, and mean average precision (MAP) of 95.21%, thereby isolating pig areas in images with notable effectiveness. Five descriptive features drawn from the imagery were examined: back area, body length, body width, average depth, and eccentricity. Leveraging the pig depth images, five regression learning algorithms—ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)—were trained to forecast mass values. AdaBoost delivered the strongest predictive performance, evidenced by a coefficient of determination (R2) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg², and a mean absolute percentage error (MAPE) of 8.45%. The experimental results confirm that machine learning models can reliably predict pig mass, providing a technical foundation for the informed management of pig production facilities.

The tractor functions as a fundamental power source for farming activities. Still, the available power is frequently not fully exploited because of poor matching between the tractor and the attached implement under actual field conditions. To improve overall system productivity, it is essential to gather information on performance-related variables characterising the tractor-implement unit. In this work, we build a real-time Instrumented Tractor Performance Monitoring System (ITPMS) that leverages the Internet of Things (IoT). The hardware foundation comprises a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer records tillage depth, whereas the three-point hitch dynamometer captures draft force values. Proximity sensors are mounted on a two-wheel-drive (2WD) tractor to measure forward speed and drive-wheel slip. A dedicated web server is set up within a Google® Firebase® project, allowing storage of data streams from all sensors via the Raspberry Pi. Alongside this, a web interface and a mobile application are constructed to present the sensor-generated data in real time. Field experiments were conducted to assess and observe the performance variables of the tractor-implement unit employing the built ITPMS. The findings reveal that the system can reliably monitor the performance variables required for the tractor-implement unit. Furthermore, the system’s ability to transmit updated information to the IoT server without delay is verified. Altogether, designing and deploying this Raspberry Pi–centred IoT framework provides a trustworthy and effective approach for real-time performance monitoring of instrumented tractors.

The surveillance of poultry movement patterns is an essential diagnostic tool for welfare evaluation, yet manual monitoring routines impose considerable temporal demands and physical strain, particularly in high-density flocks. Inadequately trained personnel may additionally fail to recognize subtle shifts in behavioral intensity, thereby overlooking critical windows for prophylactic intervention and remedial action. The present work merges conventional camera-based observation with a sophisticated automated recognition architecture to classify diverse broiler conduct within commercial housing. Deployment of a two-stage deep learning pipeline occurs as follows: initial detection localizes individual birds, followed by identification of five principal anatomical landmarks (cranial region, ventral region, bilateral limbs, and caudal appendage). From these points, a skeletal framework anchored in the ventral region is generated, yielding six angular measurements via trigonometric computations. A long short-term memory network subsequently processes these angular sequences, estimating the likelihood across behavioral categories encompassing “Standing”, “Walking”, “Resting”, “Eating”, “Preening”, and “Flapping”, with the highest-probability classification retained. To quantify the temporal distribution between stationary and dynamic states, a cascaded fuzzy-logic inference architecture was implemented, providing a resilient assessment of overall activity magnitude. Validation conducted within a mixed-gender rearing facility demonstrated that the proposed methodology attained performance floors of 85.2% for broiler categorization, 79.2% for anatomical landmark localization, and 50.8% for behavioral classification accuracy. Activity-level determinations generated by the system were consistent with evaluations by veteran poultry husbandry specialists.

Achieving optimal cannabis crop performance and product quality hinges on dependable, automated systems for leaf disease identification that allow for swift diagnosis and action. Present-day automated methods fall short of being purpose-built for the distinct difficulties inherent in spotting cannabis diseases, rarely holding up across shifting environmental contexts and varying phases of plant maturity. This paper proposes a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), grounded in an extensive data collection that mirrors the breadth of cannabis leaf pathologies and their unfolding. The design pairs non-population-based decision merging within the image preparation pipeline with population-based decision merging at the classification stage, calling upon a variety of CNN backbones. The outcome is a meaningful leap in key performance measures: HyAMIS-DLE achieves 99.58% accuracy, besting standard architectures by up to 4.16%, while also offering markedly improved resilience and a boosted Area Under the Curve (AUC) metric, enabling a clear distinction between unaffected and afflicted foliage. Rolling HyAMIS-DLE into our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) confirms its on-the-ground value, helping achieve higher harvest volumes, reduce waste, and advance ecologically sound farming practices.

Agricultural artificial intelligence is increasingly shaped by a precision paradigm that treats good farming as the increasingly fine-grained optimization of inputs, outputs, risks, and operational decisions. This paradigm is attractive because it promises measurable efficiency, predictive control, and scalable decision support across complex farming systems. In climate-sensitive agriculture, however, the same orientation can narrow what counts as a good decision. The problem is that climate-sensitive farming is not merely a technical setting in which better data automatically produce better outcomes. Farmers must act under uncertainty, ecological variability, long time horizons, and ethically contested trade-offs involving productivity, resilience, livelihoods, and environmental stewardship. These conditions expose the limits of optimization as a sufficient guide for responsible agricultural AI. This article develops a conceptual framework that shifts the guiding principle of agricultural AI from precision to prudence. Prudence is understood as practical wisdom: the capacity to deliberate well about what is fitting, responsible, and contextually appropriate when action is necessary but certainty is unavailable. The framework therefore reorients AI from prescribing optimal decisions to supporting wise and accountable judgment. The article is based on a conceptual synthesis peer-reviewed publications across agricultural ethics, responsible innovation, precision agriculture, AI governance, climate adaptation, and science and technology studies. It critiques precision agriculture as an optimization paradigm, articulates prudence as a responsible AI principle, and develops a framework for climate-sensitive decision support. Two tables contrast the precision and prudence paradigms and present the proposed conceptual framework. The central argument is not that agricultural AI should abandon data, modelling, prediction, or decision support. Rather, responsible AI for climate-sensitive farming should use these capacities to enlarge human judgment rather than replace it. A prudent AI system helps farmers deliberate under uncertainty, compare trade-offs, question recommendations, and remain accountable for decisions that are ecological, social, and moral as well as technical.

Intelligent farming systems increasingly depend on data generated through sensors, machinery, satellites, digital platforms, advisory tools, farm management software, and artificial intelligence-enabled decision-support systems. These systems promise more precise monitoring, improved resource allocation, and better responsiveness to environmental and operational uncertainty. As farm operations become more data-intensive, farm data shift from being a by-product of agricultural activity to becoming a strategic asset within intelligent farming systems. This transformation creates governance problems that cannot be resolved through technical data management alone. Farm data raise contested questions about ownership, consent, access, interoperability, platform dependence, accountability, and farmer control. These questions are especially important because data generated on farms may be used beyond the original farming context for commercial analytics, platform development, benchmarking, insurance, input recommendation, credit scoring, or supply-chain governance. The objective of this article is to develop a data stewardship framework for intelligent farms. The framework is designed to clarify how ownership, consent, access, interoperability, accountability, and farmer-control mechanisms can be organised in ways that support responsible digital agricultural innovation. It treats data stewardship as a governance function that mediates between innovation, commercial data use, and the protection of farmers’ decision-making authority. The article is based on a governance framework synthesis  peer-reviewed articles published. It does not present new empirical data. Instead, it synthesises literature on digital agriculture, smart farming, agricultural data governance, platformisation, responsible innovation, interoperability, and farmer data rights to construct a practical and conceptual governance framework. The article maps farm data as a governance problem, analyses ownership and consent issues, examines interoperability and platform dependence, and proposes a stewardship framework for intelligent farms. Three tables summarise ownership and control issues, interoperability risks, and the proposed framework. Responsible intelligent farming requires data stewardship arrangements that protect farmer control while enabling trustworthy data sharing and innovation.

Autonomous farming systems are moving rapidly from experimental prototypes into commercial and operational agricultural settings. Autonomous tractors, robotic weeders, drones, harvest-assist systems, and sensor-guided machines promise to address labour shortages, improve operational efficiency, and support more precise field management. Yet the governance arrangements needed to ensure that these systems remain safe, understandable, and accountable have not developed at the same pace. The central problem is not only whether autonomous machines can perform agricultural tasks accurately. It is whether farmers, workers, contractors, and rural communities can understand, supervise, question, override, and hold these systems accountable when they act in complex farm environments. Without deliberate attention to trust calibration, human override, explainability, and accountability, autonomous farming may produce technically impressive but socially fragile systems. This article develops a Human-Centred Governance Framework for autonomous farming. The framework is designed around four pillars: calibrated trust, meaningful human override, explainable decision-making, and clear accountability structures. Its purpose is to shift governance attention from machine capability alone toward the conditions under which autonomous systems can be responsibly integrated into agricultural work. The proposed framework shows that human-centred governance is not an optional ethical supplement to autonomous farming. It is a practical requirement for safe deployment, legitimate adoption, and accountable operation. By integrating trust, override, explainability, and accountability, the framework provides guidance for farm managers, technology developers, regulators, and agricultural service providers.

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.

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.

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.

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.

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.

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.