Purpose <p>The aim was to determine the structure of the methodological landscape of unmanned aerial vehicle (UAV)-based wheat research by converting full-text articles into machine-readable metadata and using it to track corpus-level trends in sensors, indices, analytical techniques, and applications.</p> Methods <p>A recent corpus of studies was compiled and methodological information was extracted across four domains: Sensor, Index, Technique, and Application. The pipeline established performs automatic tagging using a curated lexicon with alias coverage reinforced by context cues (index tokens, band names, wavelength mentions, and nearby formula notations). Ambiguities, such as acronym collisions, were resolved via zero-shot natural language inference using the surrounding text. Category-specific similarity thresholds were set by iterative manual inspection (sensor, 0.55; index, 0.50; and technique, 0.50). All detections were canonicalized before aggregation to prevent duplicate counts of spelling variants and aliases.</p> Results <p>The resulting metadata supported corpus-level summaries and comparative analyses across the domains. Empirically, the field has progressed from accessible RGB workflows toward multispectral and hyperspectral sensing, increased adoption of machine learning, and growing emphasis on applications, including growth monitoring, yield estimation, disease detection, phenotyping, and lodging assessment.</p> Conclusion <p>By releasing the full per-paper inventory and complete tagging lexicon as supplementary materials, this study provides a reproducible basis for monitoring methodological trends and offers a general reference framework for regions developing UAV-based agronomy.</p>