Search

Smart farming increasingly embeds artificial intelligence, machine learning, predictive analytics, remote sensing, robotics, and automated decision-support systems into agronomic decision-making. These technologies promise more precise, timely, and data-intensive forms of farm management. Their growing role suggests that agronomic decisions are no longer shaped only by farmer observation, seasonal experience, and advisory knowledge, but also by algorithmic interpretation. This article addresses the theoretical problem that AI-supported farming systems may improve technical efficiency while simultaneously reshaping farmer judgment, experiential knowledge, trust, and decision authority. Smart farming is therefore not merely a matter of technological adoption. It is also a reconfiguration of how farming problems are recognised, evaluated, prioritised, and acted upon. The objective of the article is to develop a theory of human–AI decision dependence in smart farming. The theory explains how repeated interaction with algorithmic systems can create new forms of reliance, cognitive offloading, skill transformation, and redistributed authority. It also explains why dependence on AI can be supportive, excessive, or contested depending on the quality of system design, institutional context, and farmer capacity to interpret recommendations. The article contributes a conceptual model of human–AI decision dependence in smart farming. It argues that dependence is not simply a user attitude toward technology but a sociotechnical process through which judgment, skill, trust, evidence, and authority are redistributed. This perspective helps future smart farming research examine not only whether AI systems perform accurately, but also how they change the human capacities and decision relations on which farming depends.