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The digitalisation of agri-food networks is increasingly presented as a pathway toward more intelligent, efficient, transparent, and resilient food systems. In this narrative, artificial intelligence functions as the analytical brain of the food system, converting dispersed data from farms, supply chains, markets, and consumers into actionable intelligence. This promise has become central to contemporary visions of smart farming, precision logistics, demand forecasting, and waste reduction. Yet the language of food system intelligence often conceals a more troubling political-economic transformation. The infrastructures that collect, process, and monetise agri-food data are frequently controlled by proprietary platforms, agribusiness firms, technology corporations, and data intermediaries. As a result, digitalisation may expand not only analytical capacity but also platform dependency, data concentration, and asymmetrical control over decision-making. This critical review interrogates the tension between food system intelligence and platform dependency in digital agri-food networks. It examines how AI, data infrastructures, and platform governance reshape farmer autonomy, market structure, and public authority. The review draws on critical data studies, political economy, agri-food governance, and platform studies to question the assumption that more data automatically produces more democratic or sustainable food systems. The review concludes that the central policy question is not whether AI should be used in agri-food systems, but who governs it, who benefits from it, and whose knowledge counts within it. Without deliberate governance intervention, digital agri-food networks risk becoming extractive data monopolies rather than intelligent public goods. A more democratic digital food future requires data sovereignty, farmer-centred infrastructures, public-interest governance, and stronger collective control over agri-food data systems.

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

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