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<p>Malting barley (<i>Hordeum vulgare</i> L.) varietal purity is important for stable beer quality, but major Chinese cultivars often show highly convergent seed phenotypes, with subtle seed-coat color differences. Such subtle variation makes rapid, non-destructive identification difficult for conventional convolutional neural networks. We propose Adaptive Hue-Enhanced EfficientNet (AHE-Net), a dual-stream deep learning architecture that combines color priors with adaptive Hue enhancement. AHE-Net uses unsupervised K-means clustering of normalized Hue values in the hue-saturation-value (HSV) color space to derive color-prior centers and applies a target color stretching module for nonlinear Hue remapping. On a dual-view dataset of 14,810 images from 10 barley varieties, AHE-Net achieved 98.01 ± 0.14% accuracy across six paired runs based on the same set of six random seeds, outperforming the EfficientNet-B4 baseline by 1.01 ± 0.18% points. Five-fold image-level stratified cross-validation further showed consistent improvement, with AHE-Net achieving 97.87 ± 0.25% accuracy compared with 96.29 ± 0.26% for the baseline. These results indicate that color-prior-guided Hue enhancement improves the recognition of highly similar barley varieties and supports low-cost, non-destructive raw-material screening under controlled imaging conditions.</p>

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Identification of highly similar barley varieties using color priors and adaptive enhancement

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Hao Sun,
  • Lifang Chen,
  • Guolin Cai,
  • Xianwen Huang

摘要

Malting barley (Hordeum vulgare L.) varietal purity is important for stable beer quality, but major Chinese cultivars often show highly convergent seed phenotypes, with subtle seed-coat color differences. Such subtle variation makes rapid, non-destructive identification difficult for conventional convolutional neural networks. We propose Adaptive Hue-Enhanced EfficientNet (AHE-Net), a dual-stream deep learning architecture that combines color priors with adaptive Hue enhancement. AHE-Net uses unsupervised K-means clustering of normalized Hue values in the hue-saturation-value (HSV) color space to derive color-prior centers and applies a target color stretching module for nonlinear Hue remapping. On a dual-view dataset of 14,810 images from 10 barley varieties, AHE-Net achieved 98.01 ± 0.14% accuracy across six paired runs based on the same set of six random seeds, outperforming the EfficientNet-B4 baseline by 1.01 ± 0.18% points. Five-fold image-level stratified cross-validation further showed consistent improvement, with AHE-Net achieving 97.87 ± 0.25% accuracy compared with 96.29 ± 0.26% for the baseline. These results indicate that color-prior-guided Hue enhancement improves the recognition of highly similar barley varieties and supports low-cost, non-destructive raw-material screening under controlled imaging conditions.