Kabiso Abel Chernet, Ko Cheng-Hao
Graduate Institute of Automation and Control, National Taiwan University of Science and Technology (NTUST), No. 43, Section 4, Keelung Rd, Da'an District, Taipei, 10607, Taiwan.
Sci Rep. 2025 Sep 24;15(1):32750. doi: 10.1038/s41598-025-17676-w.
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, with robust performance and minimal complexity. We evaluated SpecFuseNet on three NIR datasets: barley (1,200 samples, 24 varieties), chickpea (950 samples, 19 varieties), and sorghum (500 samples, 10 varieties) using stratified 5-fold cross-validation. The model achieved classification accuracies of 89.72%, 96.14%, and 90.67%, respectively, outperforming PCA-based machine learning models (SVM, Random Forest, XGBoost) and deep learning baselines such as standard Autoencoder (AE) and Convolutional Sparse Autoencoder (CSAE). These results demonstrate SpecFuseNet's potential as a fast, interpretable, and deployable solution for real-time classification in field-based and resource-limited settings, with a lightweight design that enables deployment on portable or smartphone-connected NIR spectrometers, supporting sustainable and precise agricultural practices.
准确识别谷物品种对于提高作物产量、简化农业工作流程以及确保全球粮食安全至关重要。近红外（NIR）光谱技术为谷物分类提供了一种快速、无损的解决方案。然而，其有效性取决于提取有意义的光谱特征。我们提出了SpecFuseNet，一种注意力增强的残差自动编码器，作为一种轻量级深度学习模型，用于提取近红外光谱特征并对谷物品种进行分类。编码器集成了融合高效通道注意力（FusedECA）和光谱残差门（SRG）以提取信息丰富的光谱特征，而镜像解码器则实现了强大的光谱重建。这种架构支持光谱重建和品种分类，具有强大的性能和最小的复杂度。我们使用分层5折交叉验证在三个近红外数据集上评估了SpecFuseNet：大麦（1200个样本，24个品种）、鹰嘴豆（950个样本，19个品种）和高粱（500个样本，10个品种）。该模型分别实现了89.72%、96.14%和90.67%的分类准确率，优于基于主成分分析（PCA）的机器学习模型（支持向量机、随机森林、极端梯度提升）以及深度学习基线，如标准自动编码器（AE）和卷积稀疏自动编码器（CSAE）。这些结果证明了SpecFuseNet作为一种快速、可解释且可部署的解决方案在基于现场和资源受限环境中进行实时分类的潜力，其轻量级设计使其能够部署在便携式或与智能手机连接的近红外光谱仪上，支持可持续和精准农业实践。
