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Data science has become a central analytical foundation of precision agriculture, enabling researchers and practitioners to transform heterogeneous farm, crop, soil, climate, machinery, sensor, and remote-sensing data into decision-relevant knowledge. This original bibliometric and thematic review examines how precision agriculture data science has evolved as a research field, how its knowledge structures are organized, and which methodological and application frontiers are shaping its future direction. The review follows a transparent bibliometric logic by distinguishing database searching, eligibility screening, metadata cleaning, keyword normalization, bibliometric indicators, thematic mapping, and interpretive synthesis. Because verified database-export counts were not available in the present working environment, the manuscript does not report invented publication totals, citation rankings, cluster sizes, or network metrics. Instead, it develops a cautious bibliometric-thematic framework grounded in the approved reference set and in clearly stated methodological safeguards. The review focuses on research evolution from early sensor analytics and remote observation toward machine learning, deep learning, big data, IoT-enabled systems, data fusion, decision support, and sustainable smart farming. It also examines knowledge structures around journals, keywords, co-occurrence logic, thematic clusters, and emerging research fronts. The main contribution is an integrated synthesis of precision agriculture data science as a field shaped by technical innovation, methodological fragmentation, data-quality constraints, interoperability challenges, explainability needs, reproducibility demands, and farmer-centered implementation concerns. The review provides a future agenda for more robust, transparent, usable, and responsible data science in precision agriculture.

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.