Attention-enhanced residual autoencoder for NIR spectral feature extraction and classification of grain varieties.

ABSTRACT: Accurate identification of grain cultivars is critical for improving crop yields, streamlining agricultural workflows, and ensuring global food security. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive solution for grain classification. However, its effectiveness hinges on extracting meaningful spectral features. We propose SpecFuseNet, an attention-enhanced residual autoencoder, as a lightweight deep learning model for extracting NIR spectral features and classifying grain varieties. The encoder integrates Fused Efficient Channel Attention (FusedECA) and a Spectral Residual Gate (SRG) to extract informative spectral features, while a mirrored decoder enables robust spectral reconstruction. This architecture supports both spectral reconstruction and cultivar classification

SUBMITTER: Kabiso AC 

PROVIDER: S-EPMC12460850 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature