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Generative artificial intelligence is rapidly entering the food sector as a family of technologies capable of producing text, images, formulations, molecular structures, process scenarios, and synthetic training data. Its relevance to food manufacturing and product development lies in its capacity to accelerate experimentation, support creativity, and extend data-driven decision-making beyond conventional prediction. Yet the evidence base remains early, fragmented, and unevenly validated. This rapid evidence review maps the emerging literature on Generative AI in food manufacturing and product development from 2017 to 2025. The objective is to identify where applications are being explored, what opportunities are being claimed, and which risks and governance needs are becoming visible. The review focuses on Generative AI as a food system technology rather than as a general digital innovation. A rapid evidence review approach was used to synthesise 29 peer-reviewed articles relevant to Generative AI, food manufacturing, food quality control, product design, reformulation, digitalisation, food safety, and rapid review methodology. The review does not present new empirical data. Instead, it offers an accelerated mapping of a fast-moving field to inform research, practice, and policy. The evidence indicates that Generative AI is most visible in synthetic image generation, large language model support for food knowledge extraction, recipe and formulation assistance, hyperspectral data augmentation, and broader Industry 4.0 food manufacturing contexts. However, much of the literature remains conceptual, proof-of-concept, or adjacent to Generative AI rather than demonstrating mature industrial deployment. Risks are concentrated around validation, hallucinated outputs, safety claims, intellectual property, biological plausibility, and weak governance alignment. The review concludes that Generative AI has significant transformative potential in food manufacturing and product development, but this potential is conditional on rigorous validation and anticipatory governance. Without domain-specific standards, human oversight, transparent documentation, and regulatory engagement, Generative AI could amplify uncertainty rather than improve food system performance. A proactive research and practice agenda is therefore essential.

Foundation models and multimodal artificial intelligence are beginning to reshape agricultural AI by enabling systems that can integrate images, text, sensor streams, remote-sensing data, environmental records, and management information. These approaches differ from earlier narrow models because they can reuse learned representations across related tasks and support richer forms of agricultural interpretation. Their potential relevance spans crop monitoring, disease detection, advisory services, automation, forecasting, and agri-food decision support. Despite this promise, the transfer of large-scale AI systems into agriculture remains complex. Agricultural environments are biologically variable, spatially heterogeneous, seasonally dynamic, and shaped by management decisions that are often poorly captured in available datasets. As a result, questions remain about data quality, annotation adequacy, domain adaptation, validation, explainability, governance, and deployment risk. This scoping review maps emerging applications, data requirements, and deployment risks associated with foundation models and multimodal AI in agriculture and agri-food systems. It focuses on how the reviewed literature frames model architectures, data modalities, application domains, validation needs, and governance concerns. The purpose is not to estimate pooled effectiveness but to chart the evidence landscape and clarify where knowledge is advancing or remains unsettled. The review uses a scoping approach to organise literature around key concepts, model types, data inputs, agricultural tasks, implementation settings, and responsible deployment concerns. It charts relationships among remote-sensing foundation models, vision-language systems, large language models, generative AI, multimodal data fusion, benchmark datasets, and agricultural decision-support systems. It also identifies gaps in field validation, interoperability, low-resource contexts, participatory governance, and trustworthy deployment. The review contributes a structured synthesis of how foundation models and multimodal AI may extend agricultural sensing, prediction, automation, and decision support. It highlights that technical capability alone is insufficient for responsible agricultural use. Robust data infrastructures, context-aware adaptation, transparent validation, and accountable governance will be central to whether these systems support sustainable and equitable agri-food transformation.

Foundation models are increasingly investigated as reusable architecture-level systems for agricultural sensing, remote-sensing interpretation, multimodal learning, knowledge access, phenotyping, monitoring, and decision-support assistance. However, the evidence remains dispersed across model families, data modalities, agricultural applications, and validation settings. This systematic review synthesises the technical architectures, data requirements, opportunities, risks, deployment challenges, evidence gaps, and research priorities associated with foundation models in smart farming and precision agriculture. Peer-reviewed journal literature was identified through structured database and publisher-platform searches, supplementary reference checking, predefined eligibility criteria, sequential screening, claim-specific evidence extraction, and thematic synthesis. Evidence was organised across language, vision, vision-language, multimodal, geospatial, remote-sensing, time-series, self-supervised, transfer-learning, and domain-adaptation approaches. The reviewed literature indicates that foundation-model approaches may support transferable representation learning, reduced annotation dependence, multimodal data integration, crop and livestock monitoring, remote-sensing analysis, plant phenotyping, agricultural knowledge services, and bounded decision-support workflows. Nevertheless, reported results are commonly task-, dataset-, region-, sensor-, or application-specific. Important limitations include agricultural data scarcity and heterogeneity, geographic and temporal domain shift, uncertain generalisation, bias, weak explainability, inadequate uncertainty communication, privacy and cybersecurity risks, unclear accountability, computing demands, and limited farmer-centred or operational evaluation. Priorities include agricultural benchmark suites, domain-specific pretraining resources, multimodal and spatiotemporal adaptation, low-resource representation, robustness testing, calibrated uncertainty, human-in-the-loop evaluation, privacy-preserving development, sustainable-computing assessment, and lifecycle governance. Foundation models represent a promising research direction, but benchmark performance or pretraining scale alone does not establish agronomic validity, operational safety, farmer trust, or deployment readiness.