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Food system transparency has become a defining demand of contemporary food governance as consumers, regulators, producers, retailers, and civil society seek clearer information about food safety, sustainability, provenance, and accountability. Yet transparency is still frequently reduced to traceability, understood as the capacity to follow a product through production, processing, distribution, and retail. Traceability remains essential because it can support food safety, provenance verification, recall management, and supply-chain visibility. However, it offers only a partial view of the decisions, data practices, and governance arrangements that increasingly shape food systems. The central problem is that traceability can show where food has been without explaining why food-system decisions are made, who benefits from data use, how algorithmic judgements are produced, or how affected stakeholders can question decisions. As artificial intelligence, digital platforms, automated decision support, blockchain systems, and data-intensive infrastructures become more embedded in food systems, transparency must include decision logic as well as product movement. A narrow traceability model may increase visibility while leaving power asymmetries, accountability gaps, and ethical risks unresolved. Transparency therefore needs to be reframed as a broader socio-technical condition. This Perspective Article argues that food system transparency must move beyond conventional traceability toward explainable, participatory, and ethics-centred AI. Explainable AI can make algorithmic decisions more understandable to producers, regulators, firms, consumers, and affected communities. Participatory design can ensure that transparency systems reflect stakeholder needs, rights, and lived realities rather than only institutional or commercial priorities. Ethics-centred governance can connect technical transparency with fairness, accountability, privacy, contestability, and public trust. The article contributes a conceptual framework for reimagining food system transparency as understandable, contestable, inclusive, accountable, and sustainability-oriented. It positions AI not merely as a tool for monitoring food supply chains but also as a governance object that must itself be explained, scrutinised, and democratically shaped. The proposed perspective highlights practical implications for food businesses, technology developers, policymakers, regulators, and civil society organisations. It concludes that transparent AI can support more trustworthy food systems only when traceability is integrated with explanation, participation, ethical responsibility, and accountable decision-making.

Intelligent agriculture is increasingly shaped by automation, data platforms, decision-support tools, robotics, sensors, and algorithmic advisory systems. These technologies are promoted as ways to improve productivity, precision, efficiency, and sustainability in farming. Yet they also alter how agricultural decisions are made, who participates in those decisions, and whose knowledge is treated as authoritative. The central problem addressed in this article is that intelligent agriculture is often presented as a technical improvement while its implications for farmer agency remain contested. Farmer autonomy, judgement, dependency, skill, and voice can be strengthened by digital tools, but they can also be narrowed when systems become opaque, prescriptive, proprietary, or difficult to contest. This tension makes farmer agency a core question for responsible intelligent agriculture. This critical interpretive review examines how automation, platformization, and participatory design reshape farmer agency. It interprets agency not as an individual trait that farmers either possess or lose, but as a socio-technical capacity formed through relations among farmers, machines, platforms, data infrastructures, advisors, markets, and governance arrangements. The review therefore focuses on how agency is enabled, constrained, redistributed, and contested. The article examines key concepts, tensions, and assumptions in the reviewed literature. It does not report new primary empirical data, but critically synthesises existing scholarship to clarify how intelligent agriculture reorganises decision-making, knowledge, dependency, participation, power, and accountability. Particular attention is given to tensions between efficiency and autonomy, automation and situated judgement, platform convenience and dependency, data-driven advice and local knowledge, and participation and tokenism. The review argues that farmer agency is redistributed through socio-technical arrangements rather than simply preserved or removed by technology adoption. Responsible intelligent agriculture requires accountable automation, fair platform governance, participatory design, recognition of local knowledge, and institutional mechanisms that give farmers meaningful voice. Farmer agency should therefore be treated as a condition of technological legitimacy, not as an afterthought to innovation.