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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.

Decision-support systems are increasingly positioned as mechanisms for translating agricultural data and analytical models into farm-management recommendations, yet their methodological quality cannot be judged through predictive performance alone. This article proposes an original methodological framework for designing and evaluating decision-support systems in precision agriculture. The framework integrates farm-management decision-context definition, stakeholder mapping, data-quality and provenance assessment, model and recommendation-logic documentation, explainability and interpretability requirements, usability and farmer-centered design, validation planning, decision-reliability assessment, adoption-barrier analysis, implementation-condition assessment, human oversight, and iterative feedback. Explainability is treated as a role-sensitive design requirement involving input visibility, output explanation, decision rationale, uncertainty communication, traceability, and appropriate explanation timing and format. Usability is addressed through farmer participation, co-design, workflow integration, accessibility, local relevance, manageable decision-support workload, training, and feedback mechanisms. Validation is extended beyond internal model testing to include external and local validation, calibration, error analysis, field-validation planning, drift monitoring, and review of the complete recommendation pathway. Adoption is interpreted as a contextual and voluntary process shaped by perceived usefulness, trust, affordability, infrastructure, digital capability, interoperability, support, and governance. Human oversight remains necessary because neither technical performance nor explanation quality independently establishes decision reliability. The framework contributes a structured method for coordinating design and evaluation activities while retaining explicit boundaries against claims of guaranteed decision quality, adoption, or deployment readiness.