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Accurate identification of rice diseases in precision agriculture using imagery remains difficult due to substantial misclassification rates, a wide range of influencing factors, and fluctuating conditions. While machine learning together with convolutional neural networks has yielded encouraging results for detecting rice diseases, these approaches have struggled to elucidate the interconnections among disease characteristics. In this work, we introduce an enhanced classification framework for rice diseases that combines a convolutional neural network (CNN) with a bidirectional gated recurrent unit (BiGRU). Concretely, a residual connection was embedded into the Inception module, its depth was enlarged, and a refined Convolutional Block Attention Module (CBAM) was incorporated. The upgraded CNN and the BiGRU were trained and evaluated separately; their outputs were then concatenated and forwarded to the classification layer for final prediction. Trials confirm that this strategy achieves 98.21% classification accuracy across four rice disease categories, delivering a robust solution for rice disease detection research.

The contemporary food system generates data at nearly every point where biological production, industrial processing, logistics, retail exchange, and consumer behaviour intersect. Farms produce sensor, satellite, machinery, weather, input, yield, and management data, while processing plants generate quality, safety, traceability, energy, water, and waste records. Downstream actors produce further streams of information through cold-chain monitoring, inventory systems, point-of-sale records, household consumption patterns, and nutrition-related choices. Despite this expanding data environment, the food system remains poor in system-wide intelligence because most data are captured, governed, and analysed within isolated organisational and technical domains. Production analytics, manufacturing intelligence, supply chain visibility, and consumer analytics have developed unevenly and are rarely connected into a coherent information architecture. As a result, decisions continue to be optimised locally rather than across the whole food system. This article proposes a conceptual framework for integrating production, processing, distribution, and consumption analytics into a unified food system intelligence capability. Food system intelligence is defined as the capacity to connect data across food system stages, convert them into actionable knowledge, and support decisions that improve productivity, sustainability, resilience, quality, and consumer value simultaneously. The article does not present new empirical data but synthesises existing literature to construct an integrative framework. The framework identifies four core data layers corresponding to production, processing, distribution, and consumption, joined through an integration layer based on standards, shared semantics, interoperable platforms, data-sharing agreements, and governance mechanisms. It also emphasises feedback flows, especially the use of downstream consumption, waste, and demand data to inform upstream production and processing decisions. Conversely, upstream sustainability, provenance, and production-practice data can enrich downstream retail, labelling, and consumer choice. The central argument is that food system intelligence is not primarily constrained by the absence of data but by the absence of integration and governance. Building this capability requires technical interoperability, institutional trust, reciprocal value creation, and a shift from stage-specific analytics to whole-system reasoning. The proposed framework offers a roadmap for connecting food system data to decisions from farm to fork and from fork back to farm.

Artificial intelligence is increasingly being embedded in agri-food supply chains through applications such as automated quality grading, risk scoring, demand forecasting, logistics optimisation, pricing analytics, and food safety alerts. These systems promise efficiency, resilience, and improved decision support, but they also reshape how decisions are made and who is affected by them. In food systems, AI governance is not only a technical issue but also a matter of safety, fairness, market access, and public trust. Existing responsible AI principles provide important normative foundations, including fairness, transparency, accountability, human oversight, and social benefit. However, these principles are often framed at a general level and do not fully address the distinctive structure of agri-food supply chains. Food chains are fragmented across farms, processors, logistics providers, retailers, technology platforms, regulators, and consumers, which makes governance more complex than in single-organisation AI deployment. This article develops a domain-specific Responsible AI Governance Framework for agri-food supply chains. The framework integrates four pillars: transparency, accountability, traceability, and human oversight. It argues that responsible AI in food must make algorithmic decisions visible, assign responsibility across distributed actors, preserve auditable records, and ensure meaningful human safeguards for high-risk decisions. The proposed framework specifies transparency requirements for different AI applications, maps accountability responsibilities across supply chain actors, connects traceability systems to auditability, and defines human oversight mechanisms for safety-critical and livelihood-sensitive decisions. Three tables operationalise the framework through transparency requirements, accountability allocation, and integrated governance mechanisms. The article concludes that responsible AI in food requires governance that is as distributed, interconnected, and safety-conscious as the supply chains it seeks to guide.

Artificial intelligence is increasingly used in food safety systems to support contamination detection, shelf-life prediction, quality classification, inspection prioritisation, and risk scoring. These applications promise faster, more sensitive, and more scalable decision support across complex food supply chains. However, the same predictive complexity that makes AI useful can also make its outputs difficult for food safety professionals, regulators, and operators to interpret. The central problem addressed in this framework article is that AI-supported food safety decisions may become opaque, uncontestable, and weakly aligned with existing structures of regulatory responsibility. When risk scores or alerts cannot be understood, challenged, or traced to relevant evidence, they may undermine rather than strengthen food safety governance. This problem is especially serious because food safety decisions involve direct public health consequences, market consequences, and legal accountability. The objective of this article is to develop an original Explainable AI Framework tailored to food safety decision systems. The framework integrates four interdependent dimensions: risk interpretation, regulatory confidence, operational accountability, and human review. It treats explainability not only as a technical property of models but also as a governance condition for responsible food safety decision-making. The article is based on a conceptual synthesis of 27 peer-reviewed articles published between 2017 and 2025 across food safety, explainable artificial intelligence, algorithmic accountability, risk assessment, and digital food safety systems. It does not present new empirical data. Instead, it constructs a design and governance framework that can guide the development, validation, deployment, and oversight of AI-supported food safety tools. The proposed framework defines four pillars: interpretable risk communication, transparent regulatory evidence, accountable operational responsibility, and contestable human review. Together, these pillars show how food safety AI systems can become not only predictive but also auditable, trustworthy, and practically usable. The article argues that embedding explainability into food safety AI is a governance and trust-building imperative rather than a purely technical enhancement.

Circular food systems require a shift from linear models of production, consumption, disposal, and remediation toward systems that continuously recover value from biological materials. This conceptual article argues that such a transition cannot rely on policy, behavioural change, or technological substitution alone. It also requires an intelligence layer capable of sensing waste generation, interpreting material quality, identifying recovery options, and coordinating decisions across fragmented food-system actors. Artificial intelligence offers this intelligence layer by enabling predictive, diagnostic, and decision-support capacities across the food chain. Rather than treating AI as a narrow automation tool, this article positions AI as cognitive infrastructure for circular food economies. Its role is to connect data on food loss, by-product composition, logistics, environmental impacts, recovery technologies, and stakeholder constraints. The article develops a conceptual framework for AI-enabled circular food systems. The framework integrates five functions: waste detection, valorisation pathway selection, resource recovery, sustainability-oriented decision support, and cross-actor coordination. These functions are presented as mutually reinforcing layers that allow food materials to be tracked, evaluated, redirected, and valorised before they become unmanaged waste. The article contributes to sustainability and circular economy scholarship by linking food-waste valorisation with material flow intelligence and multi-criteria decision support. It highlights how AI can support real-time evaluation of waste streams, match heterogeneous residues with suitable recovery pathways, and improve the coordination of producers, processors, retailers, consumers, waste managers, and policymakers. It also clarifies the risks of fragmented digitalisation when data systems are not aligned with circular outcomes. The proposed framework is intended for researchers, practitioners, and policymakers seeking to design circular food systems that are adaptive, evidence-based, and sustainability-oriented. It does not present new empirical data. Instead, it provides a conceptual basis for future empirical testing, digital platform development, policy design, and interdisciplinary research on AI-enabled food circularity.

Food systems increasingly generate consumer data through digital platforms, shopping records, dietary applications, wearable technologies, loyalty schemes, and personalised nutrition services. These data can reveal patterns of preference, dietary behaviour, product use, affordability constraints, and health-oriented needs. However, consumer data are often used for commercial targeting while remaining disconnected from product reformulation, nutrition improvement, and public health goals. AI-enabled food intelligence is frequently positioned as a tool for predicting consumer preferences or delivering personalised recommendations. This narrow use limits the potential of AI to support healthier food environments, more acceptable reformulated products, and more transparent consumer communication. A broader framework is needed to connect consumer insight with nutrition evidence, product innovation, ethical governance, and public trust. This article proposes a consumer-centred food intelligence framework linking AI, nutrition personalization, product reformulation, trust, transparency, and public health. The framework treats consumers not only as data sources but also as beneficiaries, participants, and evaluators of food-system innovation. It explains how individual and aggregated consumer intelligence can inform dietary advice, product design, reformulation priorities, and responsible communication. The article contributes a structured conceptual model for ethically transforming consumer data into health-oriented and trust-sensitive food-system decisions. It argues that AI should not only recommend diets or predict purchasing behaviour, but should also help translate nutritional evidence into acceptable products and transparent innovation pathways. Consumer-centred food intelligence is presented as a foundation for more responsible, healthier, and publicly trusted digital food systems.

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.