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

Accurate and non-destructive prediction of lettuce quality traits is essential for variety identification, germplasm utilization, and intelligent breeding. However, existing approaches relying on handcrafted features or purely data-driven models face limitations under small-sample conditions, including constrained prediction accuracy, weak interpretability, and an increased risk of overfitting. To address these challenges, we propose a knowledge-guided feature tokenizer transformer (KG-FT-Transformer) framework for hyperspectral quality trait prediction and fingerprint analysis. This framework integrates domain prior knowledge with data-driven learning, significantly improving prediction accuracy while enhancing biological interpretability. The KG-FT-Transformer employs a Transformer-based architecture integrating multi-head attention (MHA) with a gated feed-forward network (GFFN), enabling nonlinear spectral modeling and rich feature interactions. We evaluated its performance on three key quality traits: relative chlorophyll content (SPAD), soluble solids content (SSC), and moisture content (MC). The model achieved R