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Smart farming increasingly operates through digital platforms that connect farm machinery, sensors, remote-sensing services, advisory systems, agricultural inputs, data analytics, and decision-support applications. Although these arrangements may improve coordination and service integration, their governance implications cannot be evaluated through technological functionality or productivity expectations alone. This article develops an original conceptual model for examining platform governance in smart farming as a socio-technical problem involving data power, platform dependency, value creation, value capture, farmer autonomy, and accountability. The model begins with platform ecosystem mapping, actor identification, and agricultural data-flow analysis. It then assesses how data access, control, portability, interoperability, asymmetry, algorithmic influence, switching costs, and service bundling may shape dependency relationships. A parallel value pathway distinguishes ecosystem value creation from value capture and distribution among farmers, platform operators, technology providers, advisers, suppliers, complementors, cooperatives, and public institutions. Farmer autonomy is examined through decision rights, informed participation, practical agency, human oversight, trust, digital inclusion, and access to contestation and remedy. The model further links identified risks to governance mechanisms such as data stewardship, transparency, auditability, interoperability requirements, participatory governance, farmer representation, accountability structures, and dispute resolution. Its main contribution is a structured framework for evaluating smart farming platforms through power, dependency, distribution, and agency rather than platform performance alone. The model supports conceptual analysis and research design but requires contextual implementation and empirical validation.

Smart farming increasingly operates through interconnected socio-technical ecosystems rather than through isolated technologies deployed at farm level. These ecosystems involve farmers, advisors, technology and platform providers, data intermediaries, equipment and input suppliers, cooperatives, public institutions, research and extension organizations, and agri-food supply-chain actors. This article develops a conceptual ecosystem model for examining how these stakeholders contribute to data-driven agricultural innovation and how value may be created, captured, and distributed through their interactions. The model-building approach integrates stakeholder roles, multidirectional data and knowledge flows, analytics and advisory services, platform coordination, institutional support, co-production, and co-innovation. It distinguishes farmer value, platform value, supply-chain value, public value, knowledge value, and sustainability value while treating value capture and distribution as separate analytical questions. Governance dimensions include data access, control, sharing, interoperability, portability, stewardship, transparency, accountability, trust, participation, digital inclusion, dependency, and power imbalance. The principal contribution is a structured framework that connects innovation processes with governance and distributional boundaries rather than assuming that technological deployment automatically produces shared value or farmer benefit. The model is intended to support conceptual analysis, stakeholder dialogue, and future empirical research. It does not establish the effectiveness, fairness, policy suitability, commercial viability, or implementation readiness of any particular smart farming ecosystem.

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.