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This paper delivers an interview-based investigation and literature analysis examining the potential benefits and barriers to adopting Artificial Intelligence (AI) in farming enterprises. Three branches of agriculture are examined: crop cultivation, dairy farming, and beef farming. As groundwork for the study, a literature analysis re-evaluates earlier work on intelligent farming. Subsequently, an interview study with 21 participants examines receptivity and readiness for intelligent farming tools and provides technical detail on three selected AI applications in agriculture. Though centered on circumstances in Sweden, the results apply to the wider European agricultural context and beyond. The study uncovers a broad range of elements that both promote and hinder the transition. Chief among the recognized opportunities are the strategic weight intelligent farming carries on the agendas of numerous sector players, the overall shift toward software-as-a-service models through co-owned equipment, the enormous stockpiles of existing data, and the substantial eagerness among farmers for novel technology. Conversely, the study highlights primary obstacles, including technical and legal difficulties related to data ownership, looming cybersecurity threats, the need for a clearly articulated economic rationale, and the occasional lack of technical competence within the sector. The paper finds that the overarching direction signals a shift toward intelligent farming, but the pace of change will depend on how well the identified barriers are resolved.

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.

Agricultural artificial intelligence increasingly combines sensor measurements, remote sensing, farm records, predictive modelling, and digital decision support. Yet agricultural context cannot be adequately represented through machine-readable data or model performance alone. Farming decisions are also shaped by tacit knowledge, farmer expertise, local ecological understanding, seasonal experience, farm history, cultural relationships, and institutional power. This article proposes a conceptual framework for integrating these dimensions into context-aware agricultural AI without treating local or Indigenous knowledge as extractable technical inputs. The framework begins with context-aware use-case definition and farm, agroecological, cultural, and historical context mapping. It then distinguishes tacit, experiential, situated, local, Indigenous, traditional ecological, scientific, and model-based knowledge while preserving differences in authority, representation, and validation. Participatory design, farmer-centered design, knowledge co-production, human oversight, local validation, knowledge sovereignty, Indigenous data sovereignty, access control, and ethical review are positioned as interconnected requirements rather than optional additions. The framework also addresses knowledge extraction, misappropriation, decontextualization, translation loss, contextual bias, algorithmic opacity, consent ambiguity, unequal benefit distribution, and trust overclaiming. Its principal contribution is a staged architecture that links knowledge plurality with contextual interpretation, governance safeguards, uncertainty communication, feedback, and explicit decision boundaries. The framework is intended to guide conceptual development and research design. It does not authorize knowledge use, establish community or Indigenous approval, validate Indigenous knowledge through AI, guarantee cultural appropriateness or trust, or demonstrate operational or deployment readiness.