Precision Farming in Contemporary Agriculture: A Critical Review of Recent Advances, Agricultural Value, and Implementation Gaps

Abstract

Precision farming has evolved from a site-specific input-management concept into a connected agricultural management architecture that combines georeferenced sensing, Global Navigation Satellite Systems, remote and proximal sensing, Internet of Things networks, machine learning, variable-rate actuation, and increasingly autonomous machinery. This critical narrative review evaluates recent advances in these technologies and, more importantly, the extent to which technical capability translates into agronomic, environmental, economic, and institutional value. Literature published mainly from 2015 to 15 June 2026 was considered, with selected earlier studies retained where they established foundational concepts or adoption trajectories. The evidence shows a marked asymmetry in maturity. Guidance, section control, georeferenced monitoring, and several variable-rate applications are commercially established, whereas many artificial-intelligence, digital-twin, and robotic applications remain dependent on local calibration, supervised field validation, reliable connectivity, and favourable capital and service conditions. Remote sensing and sensor fusion have greatly improved the spatial and temporal visibility of crop and soil variability, but higher data density does not by itself produce better decisions. Machine-learning studies frequently report strong predictive performance, yet transferability is constrained by sparse labels, class imbalance, domain shift, inconsistent evaluation procedures, and limited independent field testing. Environmental evidence is strongest where precision technologies directly alter an input pathway, particularly variable-rate nutrient management, but broad sustainability claims remain more confident than the field evidence warrants. Profitability is similarly context dependent because gains depend on baseline variability, farm scale, equipment replacement cycles, interoperability, operator capability, and the cost of data and advisory services. The central implication is that the next phase of precision farming should be assessed as a closed-loop decision system rather than as a collection of devices. Progress will depend on outcome-based multi-site trials, uncertainty-aware analytics, interoperable data infrastructures, service models suitable for smaller farms, transparent data governance, and evaluation frameworks that connect technical accuracy to realised farm and environmental outcomes.