SE-enhanced 1-D CNN with full-band hyperspectral imaging for rapid and accurate maize seed variety classification.

Introduction: Accurate identification of maize seed varieties is essential for enhancing crop yield and ensuring genetic purity in breeding programs. Methods: This study establishes a non-destructive classification approach based on hyperspectral imaging for discriminating 30 widely cultivated maize varieties from Northwest China. Hyperspectral images were acquired within the 380-1018 nm range, and the embryo region of each seed was selected as the region of interest for spectral extraction. The collected spectra were preprocessed using Savitzky-Golay (SG) smoothing. Several machine learning models-KNN, ELM, and a two-layer convolutional neural network integrated with squeeze-and-excitation (SE) attention modules (CNN2c-SE)-were constructed and compared. Results: Results demonstrated that the CNN2c-SE model utilizing full-spectrum data achieved a superior classification accuracy of 93.89%, significantly outperforming both conventional machine learning models and feature-waveband-based approaches. Discussion: The proposed method offers an effective and efficient tool for high-throughput, non-destructive maize seed variety identification, with promising applications in seed quality control and precision breeding.