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Smart farming has entered a phase in which agricultural fields, machines, livestock systems, greenhouses, and advisory platforms increasingly generate continuous streams of digital information. Sensors promise greater precision, faster monitoring, and more responsive management, yet their proliferation has not automatically improved farmer decision-making. Many farms now face a paradox in which more data are available than can be interpreted, trusted, or acted upon in routine agronomic practice. The central problem addressed in this article is the widening gap between sensor abundance and actionable agronomy. Smart farming systems often excel at data collection but remain less effective at filtering, prioritising, and translating data into decisions that farmers can use under time, labour, weather, and resource constraints. This creates a data-to-decision gap that reduces the practical value of agricultural digitalisation. The objective of the article is to propose a conceptual pathway for reducing data overload in smart farming systems. The pathway moves from sensor data acquisition to information filtering, decision relevance, decision translation, and actionable agronomic delivery. It is designed to help researchers, developers, advisors, and farm managers evaluate not only whether data are collected, but whether they are converted into usable decisions. The proposed pathway identifies four sequential stages: sensor data acquisition, information filtering and relevance assessment, decision translation, and actionable agronomy delivery. Three tables support the framework by cataloguing sensor sources, classifying filtering strategies, and presenting the full pathway. The article concludes that the value of smart farming should be judged less by the amount of data generated and more by the quality, usability, and timeliness of the agronomic decisions enabled.

Precision agriculture is increasingly shaped by sensors, remote sensing, artificial intelligence, automated machinery, digital platforms, and data-driven decision support. However, technical capability alone does not establish whether a smart farming system is usable, locally appropriate, socially responsible, environmentally beneficial, resilient under disruption, or worthy of farmer reliance. This conceptual article proposes a human-centered precision agriculture framework that places farmers, farm workers, agricultural advisors, local knowledge, farm conditions, usability, accessibility, explainability, decision confidence, human oversight, and responsible governance at the center of system design. The framework integrates sustainability and resilience assessment with user-centered artificial intelligence, participatory design, transparent data practices, uncertainty communication, accountable decision support, and iterative learning. It distinguishes decision confidence from decision quality, explainability from guaranteed trust, participation from assured adoption, and technological potential from demonstrated sustainability or resilience. The proposed structure organizes human-centered smart farming into interconnected stages covering problem definition, stakeholder mapping, farmer-needs assessment, data and system context, sustainability and resilience requirements, explainability design, participation, oversight, governance, evaluation, and feedback. Its main contribution is a socio-technical framework for structuring research and design priorities while maintaining explicit boundaries around empirical validation, farmer autonomy, responsible implementation, and claims of system readiness.