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Precision farming increasingly depends on diverse agricultural data sources, yet these sources are often discussed as isolated technologies rather than as complementary inputs within decision-support systems. This article develops a conceptual taxonomy of agricultural data sources for precision farming by classifying remote sensing, geospatial layers, IoT sensors, machinery-generated records, climate and weather data, soil data, environmental observations, farm-management records, and farmer knowledge. The taxonomy approach is literature-grounded and organized around data-generation mechanism, spatial and temporal scale, resolution, format, uncertainty, interoperability requirements, ownership concerns, farmer usability, decision-support relevance, and analytics readiness. Rather than treating data as automatically useful, the article argues that each source family has distinct strengths, limitations, validation needs, and practical conditions of use. Remote sensing can support spatial monitoring, IoT sensors can provide high-frequency observations, machinery records can document operational activity, climate and soil data can contextualize crop response, and farmer knowledge can support interpretation, feasibility assessment, and local validation. The contribution is an original data-source taxonomy that helps researchers, advisors, farmers, and technology developers evaluate how agricultural data sources can be combined responsibly for precision farming. The taxonomy supports more coherent data integration, more transparent AI-ready agricultural systems, and more farmer-centered decision support.

Autonomous farming is emerging as a precision agriculture frontier in which sensing, environmental awareness, reasoning, decision logic, robotic action, and adaptive feedback are linked within increasingly integrated technical systems. Yet autonomy in agriculture cannot be reduced to unattended machine operation because field environments are variable, data quality is uneven, physical actions are consequential, and farmers and operators retain essential contextual and supervisory roles. This article develops an original conceptual model for analysing autonomous farming systems as supervised socio-technical architectures rather than self-sufficient farm managers. The model is constructed through conceptual synthesis of agricultural robotics, computer vision, machine learning, navigation, control, human–robot interaction, and responsible innovation scholarship. It organizes autonomy into connected layers: use-case definition; sensing and environmental awareness; data quality and fusion; reasoning, planning, and uncertainty interpretation; acting, control, and robotic execution; learning, feedback, and drift monitoring; and human oversight, safety, accountability, and governance. The main contribution is a closed-loop account of how farm observations may become bounded robotic actions only when perception is validated, reasoning is contextually interpreted, plans remain within operating constraints, execution is monitored, and human intervention remains available. Learning is treated as governed model updating rather than automatic self-improvement. The model therefore clarifies both enabling relationships and non-guarantee boundaries: conceptual integration alone does not establish safe autonomous operation, productivity improvement, legal sufficiency, or deployment readiness. It offers a structured basis for future research, local validation planning, supervisory design, and responsible assessment of autonomous farming within precision agriculture.