{
  "abstract": "Background: Soil chemical composition is a key indicator of soil fertility, yet conventional physicochemical analysis is time-consuming and costly. Although near-infrared (NIR) spectroscopy enables rapid, non-destructive detection, traditional linear models struggle with the non-linear nature of high-dimensional spectral data, and deep-learning models often suffer from a \"black-box\" problem.\n\nMethods: A one-dimensional residual convolutional neural network (1D-Res-CNN) was constructed, and the output feature maps of intermediate convolutional layers were visualized to reveal how the model learns spectral features. One-dimensional spectral sequences were mapped into twodimensional images via Gramian Angular Summation Field (GASF) and Continuous Wavelet Transform (CWT); after systematically comparing encoding performance across spectral sub-intervals, the optimal range was selected to feed a two-dimensional residual CNN (2D-Res-CNN) regression model. Using SNV-preprocessed Fourier-transform NIR spectra and an SPXY-partitioned dataset, the proposed framework was benchmarked against partial least squares regression (PLSR), k-nearest neighbors (KNN), and random forest (RF).\n\nResults: First-layer kernels in the 1D-Res-CNN automatically captured spectral features corresponding to functional-group absorption peaks, supporting the physical interpretability of feature extraction. GASF-encoded images in the 5880~4000 cm ⁻¹ range exhibited the highest feature density and outperformed CWT encoding. The 2D-Res-CNN achieved prediction-set R p 2 of 0.87, 0.91, 0.91, 0.93, and 0.88 for soil pH, labile organic carbon, total nitrogen, organic matter, and available phosphorus, respectively, with lower RMSEP than both the 1D-CNN and PLSR.\n\nConclusion: By enhancing spectral feature expression through visual encoding and providing preliminary insight into model behavior via layer-wise feature-map visualization, this work offers an interpretable technical strategy for rapid quantitative analysis of soil chemical composition by NIR spectroscopy.",
  "abstract_shown": true,
  "affiliations": [
    "Zhengzhou Tobacco Research Institute of CNTC",
    "College of Chemistry and Chemical Engineering, Hunan University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Xiong Li"
    },
    {
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        0
      ],
      "name": "Junwei Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Ran Wan"
    },
    {
      "affs": [
        0
      ],
      "name": "Le Zhao"
    },
    {
      "affs": [
        1
      ],
      "name": "Zengping Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9nadwdzrgfmx9.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1953112",
  "fulltext_url": "https://europepmc.org/articles/PMC13713288",
  "id": "9nadwdzrgfmx9",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1953112/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "1602|Spectroscopy and Chemometric Analyses"
    ]
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  "openalex": "W7221817334",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1953112/pdf",
  "pmcid": "PMC13713288",
  "pmid": "42952570",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9nadwdzrgfmx9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1953112",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1953112/full"
    },
    {
      "first_seen": "2026-10-31T00:20:34Z",
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      "url": "https://europepmc.org/article/MED/42952570"
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    {
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      "url": "https://doi.org/10.3389/fpls.2026.1953112"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1602|Spectroscopy and Chemometric Analyses",
      "subfield": "analytical"
    }
  ],
  "title": "Explainable residual CNN with Gramian Angular Field encoding for rapid quantification of soil properties using near-infrared spectroscopy",
  "title_text": "Explainable residual CNN with Gramian Angular Field encoding for rapid quantification of soil properties using near-infrared spectroscopy",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:20:59Z",
  "url": "https://research.draconitic.com/paper/9nadwdzrgfmx9",
  "venue": "Frontiers in Plant Science",
  "versions": []
}