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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.

Foundation models are becoming relevant to precision agriculture because they can connect sensing, remote sensing interpretation, multimodal data integration, knowledge retrieval, advisory communication, and farm decision-support workflows. Their broad capability, however, does not establish agronomic validity, trustworthy performance, operational safety, or deployment readiness. This article develops an original governance and technical safeguard framework for responsible agricultural foundation models. The framework treats these models as potential components of decision infrastructure and integrates trustworthy artificial intelligence requirements with data governance, validation, human oversight, farmer participation, monitoring, and explicit claim boundaries. It identifies requirements concerning data quality, provenance, documentation, transparency, explainability, interpretability, uncertainty communication, robustness, generalization, bias, fairness, privacy, cybersecurity, accountability, auditability, and traceability. It also maps risks arising from opaque pretraining data, uncertain data control, hallucination, factual error, agronomic invalidity, platform dependency, model drift, data drift, and unclear responsibility. Technical safeguards include data screening, domain-adaptation assessment, external and local validation, robustness testing, bias and fairness review, factual and agronomic checking, human-in-the-loop review, and continuing monitoring. Responsible deployment principles add purpose limitation, context sensitivity, incident response, withdrawal planning, and non-autonomy boundaries. The contribution is a structured conceptual framework that connects governance questions with technical evidence and accountable actors without claiming legal compliance, guaranteed trustworthiness, farmer approval, or deployment readiness.