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Artificial intelligence is increasingly embedded in smart farming through decision-support systems that integrate sensor data, remote sensing, machinery data, farm records, and predictive analytics. Yet agricultural decision-making cannot be reduced to automated recommendations because farm decisions are shaped by agronomic uncertainty, local field conditions, economic constraints, environmental goals, labor availability, machinery limitations, farmer experience, advisory relationships, and risk preferences. This conceptual framework article develops a farmer-centered account of human-in-the-loop intelligence for AI decision support in smart farming. The framework positions AI not as a substitute for farmer judgment but as one component of an iterative socio-technical decision process. It emphasizes farmer judgment, experiential knowledge, contextual validation, explainability, uncertainty communication, human oversight, trust calibration, advisor mediation, feedback, accountability, and adaptive decision-making. The article argues that human-in-the-loop intelligence is more than human approval of algorithmic outputs; it involves interpretation, questioning, correction, practical adaptation, and responsibility for situated decisions. By integrating agricultural AI, precision agriculture decision support, farmer knowledge, responsible innovation, and human-centered design perspectives, the manuscript contributes a structured conceptual basis for designing smart farming systems that preserve farmer agency while improving the practical relevance, transparency, and accountability of AI-supported decisions.

Artificial intelligence is increasingly embedded in smart farming systems, yet explanation requirements are often treated as a single technical attribute rather than as stakeholder-dependent needs. This article develops a conceptual taxonomy of explainability requirements across agricultural decision contexts. The taxonomy approach distinguishes stakeholders by their roles, decision consequences, explanation purposes, required explanation objects, usability expectations, accountability needs, trust risks, and implementation boundaries. Farmers may require accessible, locally relevant, actionable, and uncertainty-aware explanations that preserve autonomy. Agronomists and advisors may require model logic, input relevance, data-quality information, output rationale, limitations, and evidence supporting professional validation. Regulatory stakeholders may require traceability, documentation, auditability, risk justification, and clear responsibility allocation. Platform providers, technology providers, data intermediaries, system operators, and data stewards may require lifecycle transparency, monitoring information, provenance records, version histories, and incident reconstruction. Supply-chain and sustainability actors may require interpretable provenance and decision boundaries without treating explanation as proof of quality, sustainability, or compliance. The taxonomy also identifies failures including under-explanation, over-explanation, misleading explanations, uncertainty omission, explanation overload, false confidence, provenance opacity, accountability gaps, and trust miscalibration. Its main contribution is a structured account of how explanation requirements change across stakeholders and decision settings. The taxonomy is conceptual and requires stakeholder validation, usability testing, governance assessment, and domain-specific evaluation before operational application.