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<p>As an important agricultural product, the quality and commercial value of soybeans largely depend on their geographical origin. Addressing the issues of low detection efficiency, complex operation, and long detection times in traditional soybean origin traceability, this study proposes a novel method for the rapid and non-destructive detection of soybeans from different producing areas by the synergetic application of near-infrared (NIR) spectroscopy and electronic eye (EE) combined with an improved CNN-VMamba mixed network (CVMNet). First, the spectral signal and visual image of soybean samples are collected by an NIR spectroscopy and EE, respectively. Subsequently, the Gramian Angular Difference Field (GADF) is implemented to convert the 1D spectral signal into a 2D spectrogram for effectively characterizing inter-wavelength correlation patterns and nonlinear spectral dependencies within the signals. The spectrograms and EE images are then input into the CVMNet model to perform pattern recognition. This model first utilizes hybrid multi-scale dilated dynamic convolution (H-MDDC) to effectively capture local detailed features within the images. The VMamba structure is then employed to exploit long-range dependency among pixels and optimize computational efficiency. A modality interaction module (MIM) is then introduced to facilitate information interaction between the bi-modal features. Finally, a co-attentional modality fusion (CoAMF) mechanism is adopted to perform information fusion and classification on the NIR and EE information. The experimental results indicate that, compared to using NIR spectroscopy or EE individually, the proposed method yields higher recognition accuracy for detecting soybean origin, with its detection accuracy reaching 98.67%. This study provides a rapid and accurate analysis method for soybean origin traceability detection, and offers new insights for the traceability detection of other grain products.</p>

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Synergetic application of near-infrared spectroscopy and electronic eye combined with an improved CNN-VMamba mixed network for soybean origin traceability

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Tianrui Han,
  • Zhiqiang Wang,
  • Suchao Xu,
  • Jingbao Wang,
  • Junying Dou,
  • Yubin Lan

摘要

As an important agricultural product, the quality and commercial value of soybeans largely depend on their geographical origin. Addressing the issues of low detection efficiency, complex operation, and long detection times in traditional soybean origin traceability, this study proposes a novel method for the rapid and non-destructive detection of soybeans from different producing areas by the synergetic application of near-infrared (NIR) spectroscopy and electronic eye (EE) combined with an improved CNN-VMamba mixed network (CVMNet). First, the spectral signal and visual image of soybean samples are collected by an NIR spectroscopy and EE, respectively. Subsequently, the Gramian Angular Difference Field (GADF) is implemented to convert the 1D spectral signal into a 2D spectrogram for effectively characterizing inter-wavelength correlation patterns and nonlinear spectral dependencies within the signals. The spectrograms and EE images are then input into the CVMNet model to perform pattern recognition. This model first utilizes hybrid multi-scale dilated dynamic convolution (H-MDDC) to effectively capture local detailed features within the images. The VMamba structure is then employed to exploit long-range dependency among pixels and optimize computational efficiency. A modality interaction module (MIM) is then introduced to facilitate information interaction between the bi-modal features. Finally, a co-attentional modality fusion (CoAMF) mechanism is adopted to perform information fusion and classification on the NIR and EE information. The experimental results indicate that, compared to using NIR spectroscopy or EE individually, the proposed method yields higher recognition accuracy for detecting soybean origin, with its detection accuracy reaching 98.67%. This study provides a rapid and accurate analysis method for soybean origin traceability detection, and offers new insights for the traceability detection of other grain products.