Multi-Path Attention Fusion Transformer for Spectral Learning in Corn Quality Assessment.

Abstract

Accurately modeling the nonlinear relationships between near-infrared (NIR) spectral signatures and biochemical traits in corn remains a major challenge. A key difficulty lies in capturing multi-scale contextual dependencies-ranging from local absorption peaks to global spectral patterns-that jointly determine quality constituents such as protein and oil. To address this, we propose SpecTran, a spectral Transformer network specifically designed for NIR regression. SpecTran integrates three key components: adaptive multi-scale patch embedding which extracts spectral features at multiple resolutions to capture both fine and coarse patterns, spectral-enhanced positional encoding which preserves wavelength order information more effectively than standard encoding, and hierarchical feature fusion for robust multi-task prediction. Evaluated on the public Eigenvector corn dataset, SpecTran had a performance across four key traits-moisture, starch, oil, and protein-with an average R2 of 0.483. It reduced the RMSE by 11.2% for protein and 10.7% for oil compared to the best-performing baseline, which is the standard Transformer model. These results demonstrate SpecTran's superior ability to model complex spectral dynamics while providing interpretable insights, offering a reliable framework for NIR-based agricultural quality assessment.

Plant phenotyping relevance

トウモロコシのNIRスペクトルから水分・デンプン・油・タンパク質という種子品質形質を推定するTransformer手法を開発し、公開データセット上でベースライン比較検証しており、形質取得・推定法が研究の中心である。

Code and data availability

The paper uses the public Eigenvector corn NIR dataset and provides an authors' GitHub repository containing the spectral data, reference trait values, preprocessing scripts, and model implementation code.