Diagnostic framework for assessing Smart Agricultural Technology adoption

Farmers lack systematic methods to assess digital technology suitability for their specific contexts, with prospective indicators for technology–farm fit scarce in literature. This paper presents a diagnostic framework assessing adoption feasibility across three empirically grounded dimensions: context matching, infrastructure readiness, and farmer capability/economic viability. The framework was developed via bibliometric analysis (814 publications, 2014-2023, with 20 in-depth case studies), benchmarking ten existing indices, and expert validation (22 specialists assigning importance weights 1-5, consolidated by modal value). As proof-of-concept, two developer/adopter instruments were applied to contrasting Embrapa technologies: Aquicultura Certa (AI-based software) and an electronic fruit-harvest tracking device (hardware). The software scored 3 (moderate feasibility) due to electricity, connectivity, and training needs, while the hardware scored 4 (high feasibility) due to simpler prerequisites. Developer and adopter assessments converged for hardware but moderately diverged for software, illustrating perspective-specific barriers. These pilots demonstrate internal logic and usability but do not constitute predictive validation; broader applicability requires multi-technology, independent, and longitudinal testing. The framework enables extension services to match technologies to producers, developers to refine solutions for resource-limited settings, and policymakers to target infrastructure investments, bridging the gap between technological availability and appropriate adoption to support evidence-based decisions.