{
  "abstract": "Introduction: Canadian wild rice kernels exhibit diverse postharvest variations, including breakage, discoloration, insect damage, and incomplete dehulling, making accurate and non-destructive classification important for screening and grading. However, RGB imaging may be insufficient for distinguishing kernels with subtle or overlapping visual characteristics, while visible–near-infrared (VNIR) spectra provide complementary information but contain substantial redundancies. Moreover, conventional multimodal fusion strategies based on direct concatenation or fixed weighting cannot readily adapt to sample-specific differences in modality contribution.\n\nMethods: To address these limitations, this study proposed a lightweight RGB–VNIR dual-modal fusion framework for fine-grained quality-category classification of Canadian wild rice kernels. A Coordinate Attention-enhanced MobileNetV3-Small was employed to extract spatial phenotypic features, while a 1D-GhostResNet-SE was used to model continuous VNIR spectral responses. A Cross-Modal Contribution-Aware Recalibration Fusion Head (CMCR-FH) was further introduced to adaptively regulate the contributions of the two modalities. The framework was evaluated on 2,048 paired RGB–VNIR samples from eight categories using stratified five-fold cross-validation.\n\nResults: The proposed framework achieved a mean accuracy of 98.68% ± 0.22% and a Macro-F1 score of 98.69% ± 0.22%, representing numerical improvements of 4.93 and 4.97 percentage points, respectively, over the baseline model, while requiring only 1.236 M parameters and 63.537 M multiply–accumulate operations. Ablation experiments demonstrated complementary contributions from visual feature enhancement, lightweight spectral representation, and sample-adaptive multimodal fusion, with CMCR-FH producing the largest numerical performance gain among the evaluated components. Grad-CAM++ and SHAP analyses further indicated that the model jointly utilized visible phenotypic abnormalities and class-dependent spectral response patterns for discrimination.\n\nDiscussion: These results demonstrate that integrating complementary RGB and VNIR information with sample-adaptive fusion can improve fine-grained quality-category classification while maintaining low computational complexity. The proposed framework provides an effective balance among classification accuracy, computational efficiency, and interpretability, offering a promising approach for multimodal postharvest quality-category classification and grading of Canadian wild rice kernels.",
  "abstract_shown": true,
  "affiliations": [
    "The Tourism College of Changchun University, College of Artificial Intelligence , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhenhua Qiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinting Zou"
    },
    {
      "affs": [
        0
      ],
      "name": "Dongxu Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Chunyun Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dh9g8rmtk8hq6.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1973124",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669176/?tool=EBI",
  "id": "dh9g8rmtk8hq6",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13669176",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1602|Spectroscopy and Chemometric Analyses"
    ]
  ],
  "openalex": "W7215059690",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669176/pdf/?tool=EBI",
  "pmcid": "PMC13669176",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/dh9g8rmtk8hq6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:18Z",
      "native_id": "PMC:PMC13669176",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13669176"
    },
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1973124",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1973124/full"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1602|Spectroscopy and Chemometric Analyses",
      "subfield": "analytical"
    }
  ],
  "title": "Adaptive RGB–VNIR spectral fusion for non-destructive quality assessment of Canadian wild rice kernels",
  "title_text": "Adaptive RGB–VNIR spectral fusion for non-destructive quality assessment of Canadian wild rice kernels",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/dh9g8rmtk8hq6",
  "venue": "Frontiers in plant science",
  "versions": []
}