Technology Acceptance Level (TAL): A human-centred metric for evaluating trustworthy artificial intelligence in agriculture

Artificial intelligence ( AI ) systems are increasingly proposed for agricultural decision support, precision farming and precision livestock farming ( PLF ), yet large-scale and durable adoption by practitioners remains the exception rather than the rule. Established maturity scales such as the Technology Readiness Level ( TRL ) capture the engineering progression of a technology from basic principles to operational deployment, but they are agnostic to whether end users actually rely on the resulting system in their daily work. This paper introduces the Technology Acceptance Level ( TAL ) as a complementary, human-centred metric for evaluating the usability, trustworthiness and operational uptake of AI in agriculture. TAL is defined as a nine-level ordinal scale, anchored in the Technology Acceptance Model and the European Union ethics guidelines for Trustworthy AI , that captures the trajectory from initial exposure of a system to a practitioner up to its stable, autonomous integration into routine decision-making. Each level is operationalised by a small set of measurable criteria spanning perceived usefulness, perceived ease of use, transparency, reliability under changing conditions, and alignment with agricultural workflows. We provide a mathematically grounded aggregation rule based on a non-compensatory, minimum-of-dimensions principle that prevents a single weak dimension from being masked by strong scores elsewhere, and we derive a confidence interval for TAL estimates from practitioner survey data. The framework is illustrated through three representative use cases — variable-rate nitrogen management in arable cropping, vision-based fruit detection for selective harvesting, and computer-vision-based lameness detection in dairy cattle — each a distinct agricultural AI system, chosen to demonstrate how systems of comparable engineering maturity can occupy very different acceptance levels and, in particular, how the dimension that limits acceptance differs systematically across deployment contexts. By explicitly addressing the human-in-the-loop, TAL is proposed as a diagnostic instrument — subject to empirical validation — for identifying adoption barriers, prioritising iterative system improvement, and steering the transition from technically functional prototypes to practically accepted, trustworthy AI in the agri-food sector. The framework is developed and illustrated within a high-income, European-influenced agricultural context, and its scores in this article are illustrative rather than empirically measured. Two limitations bound these claims and should be read alongside them: the framework has not yet been validated against field data, and it is scoped to decision-support and monitoring AI , so that systems relying on physical actuation — and settings beyond the Western commercial context in which it was developed — require further extension.