Abstract
Precision agriculture (PA) is evolving from a set of site-specific technologies into an integrated management paradigm for addressing agricultural heterogeneity under resource and climate constraints. This review addresses a central question: how can heterogeneous agricultural observations be transformed into reliable, scalable, and actionable management decisions? Through a structured synthesis of research on sensor networks, unmanned aerial vehicle (UAV) and satellite remote sensing, artificial intelligence (AI), and big data analytics, we examine their roles across soil management, crop monitoring, irrigation, and production management, and assess the technological and institutional conditions governing implementation. The synthesis indicates that the principal value of PA lies not in individual technologies but in coupling multiscale observation, heterogeneity-aware analysis, decision support, and field-level actuation to improve the alignment of resource inputs with spatially and temporally variable crop and soil requirements. Its broader application remains constrained by investment costs, data interoperability and governance, model transferability, digital infrastructure, and uneven technical capacity. Accordingly, we propose an observation–heterogeneity–infrastructure framework that links sensing and remote observation with AI-enabled interpretation and interoperable implementation systems, providing a conceptual basis for understanding PA as an adaptive agricultural management architecture rather than a technology portfolio. This framework integrates technological, operational, and institutional dimensions and offers a basis for evaluating the scalability, robustness, and sustainability of precision agriculture across heterogeneous production contexts.
