KG-FT-Transformer: a knowledge-guided feature tokenizer transformer for hyperspectral prediction of lettuce quality trait and fingerprint analysis

values of 0.9534, 0.9185, and 0.9226, with corresponding residual predictive deviation (RPD) values of 4.63, 3.50, and 3.60, outperforming all baseline models and demonstrating stable and consistent prediction performance. Moreover, pixel-wise predictions were used to construct quality trait fingerprints through pseudo-color mapping, intuitively visualizing the spatial distribution and varietal specificity of traits. SPAD and SSC exhibited visually consistent central aggregation patterns, while MC revealed distinct spatial variations among cultivars. These fingerprint-based representations provide spatially informed, qualitative references that may assist the interpretation of DUS-related (Distinctness, Uniformity, Stability) trait characteristics. Overall, this study demonstrates the potential of integrating hyperspectral prediction with quality fingerprinting for non-destructive quality assessment and breeding-oriented analysis, and provides a complementary perspective for germplasm identification and crop improvement.