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Swarm-based unmanned aerial vehicle (UAV) systems are increasingly recognized for their capabilities in extensive IoT and precision agriculture operations, notably in collaborative surveillance and the tracking of multiple moving objects across outdoor terrains. Nevertheless, prevailing coordination mechanisms tend to assume flawless communication channels, disregarding practical network disturbances including congestion, dropped packets, and transmission delays. Such disruptions hinder the prompt relay of data among UAVs and the terrestrial control station, causing control instructions to arrive late or not at all. The outcome is a breakdown in coordination fidelity and a steep decline in tracking effectiveness when these systems are fielded. To remedy this shortcoming, we put forward CSOOC (Communication-State Driven Online–Offline Coordination with Congestion Control), a hybrid governance scheme that fuses centralized learning-powered decision processes with decentralized rule-governed behaviors, enabling UAVs to tailor their actions to instantaneous network status. The CSOOC framework is built around three core elements: (1) an online component providing centralized orchestration while communication remains dependable, (2) an offline, profit-guided movement policy relying on localized Gaussian maps to sustain autonomous target pursuit when connectivity drops, and (3) congestion mitigation rooted in STAR (Stratified Transmission and RTS/CTS), which couples staggered transmission timing with RTS/CTS handshake protocols to bolster uplink robustness. A cohesive co-simulation infrastructure is constructed, linking network-layer communication with swarm-level control and coordination dynamics. Trials reveal that CSOOC delivers a mean observation ratio of 39.7%, outperforming reference algorithms by margins of 4.4%–11.13%, while also boosting network dependability through markedly improved packet delivery success under saturated channel conditions. These outcomes confirm that CSOOC successfully spans the disconnect between algorithm performance within simulators and the operational realities of UAV collectives in communication-challenged settings.

Driven by the worldwide surge in fish maw commerce, authenticity verification has emerged as an urgent concern due to mounting adulteration incidents. Conventional approaches to verification—whether sensory-based, chemically oriented, or molecular—suffer from practical deployment constraints. The present study reframes fish maw genuineness assessment as a detection task within the object recognition paradigm and introduces RDT-YOLO, a streamlined detection model grounded in YOLO11n architecture. Addressing the twin difficulties of insufficient nuanced texture representation and processing overhead in this domain, we engineer hierarchically structured reparameterized modules for feature extraction that deploy reparameterization tactics to amplify multi-resolution texture discrimination. To counteract degradation of information arising during cross-scale feature merging, a Dynamic Adaptive Multi-Scale Pyramid Processing (DAMSPP) component is devised, integrating adaptive convolution dynamics that facilitate responsive feature synthesis. Concurrently, an Adaptive Task-Aligned Detection Head (ATADH) is formulated, fusing interactive task mechanisms with weight-shared convolutions to achieve parameter economy alongside precision gains. Moreover, Wise-ShapeIoU, a novel loss formulation, is constructed by injecting Wise-IoU's focusing coefficient into the Shape-IoU framework, thereby sharpening detection fidelity through refined contour alignment. Benchmark experiments confirm that RDT-YOLO delivers precision of 91.9%, recall of 89.6%, and [email protected] of 94% while slashing parameter count, storage footprint, and FLOPs by 75.6%, 73.8%, and 63.8%, respectively, against YOLO11s. Comparative trials versus YOLOv10s and YOLOv12s reveal [email protected] elevations of 0.8 and 0.5 percentage points. This investigation furnishes an automated pipeline for verifying fish maw genuineness, bearing implications extending across food integrity applications.

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.