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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.

Remote sensing and machine learning have become prominent components of smart farming research, generating an expanding body of review literature across agricultural monitoring and prediction applications. This umbrella review synthesises review-level evidence concerning crop monitoring, disease detection, yield forecasting, and decision support while examining the methodological conditions that shape evidence strength and transferability. Peer-reviewed systematic reviews, scoping reviews, structured reviews, surveys, meta-reviews, and other eligible review-level articles were identified through searches of major academic databases and publisher platforms. Evidence was organised by application domain, sensing modality, analytical method, validation design, generalisation, uncertainty, reproducibility, and decision-support relevance. Crop-monitoring reviews describe extensive use of satellite, unmanned aerial vehicle, multispectral, hyperspectral, thermal, and multimodal data for crop mapping, growth assessment, phenotyping, stress monitoring, and related applications. Disease-detection reviews report rapid expansion of image-based machine learning and deep learning, but repeatedly identify data scarcity, class imbalance, curated-image dependence, and limited cross-context validation. Yield-forecasting reviews identify recurring integration of remote sensing, weather, soil, crop-model, and management information, although forecast horizons, spatial and temporal validation, uncertainty communication, and decision relevance remain inconsistent. Across domains, reported technical promise is moderated by heterogeneous datasets, benchmark inconsistency, review overlap, incomplete reproducibility, and limited evidence of transfer across crops, regions, seasons, and acquisition conditions. Review-level evidence supports a substantial research opportunity for remote sensing and machine learning in smart farming, but it does not independently establish field readiness, diagnostic reliability, operational forecasting performance, or deployment readiness. Stronger review reporting, external validation, transparent benchmarking, uncertainty assessment, reproducible workflows, and responsible decision-support evaluation are required.