AUTHOR=Zhang Linzhe , Liu Chengzhong , Han Junying , Sun Kai , Feng Yongqiang 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1587845

DOI=10.3389/fpls.2025.1587845

ISSN=1664-462X

ABSTRACT=IntroductionAccurate identification of maize seed varieties is essential for enhancing crop yield and ensuring genetic purity in breeding programs.MethodsThis 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.ResultsResults 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.DiscussionThe 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.