{
  "abstract": "Winter wheat yield reflects the combined development of canopy structure and physiological status, making multitemporal structural and spectral observations potentially valuable for pre-maturity prediction. We used a ground-based phenotyping platform to acquire five-band multispectral imagery at 14 growth stages and LiDAR point clouds at 10 growth stages. Both data streams were aligned to a common phenological axis and integrated in a temporal graph convolutional network (Temporal-GCN) that encoded canopy spectral responses, three-dimensional structural states, and signed changes between adjacent stages. A stagewise cutoff analysis evaluated yield prediction from erecting through maturity. Multitemporal multispectral trajectories captured SPAD dynamics, whereas LiDAR directly provided plant height and achieved high accuracy in LAI estimation, indicating complementary sensing responses. In three-fold cross-validation grouped by complete biological replicate, the fused model comprising VARI, EVI, NDRE, and seven point-cloud structural variables reached R ² = 0.883 at heading and its highest full-season cutoff point estimate at early grain filling (R ² = 0.903, RMSE = 25.52 kg mu ⁻¹ , MAE = 20.84 kg mu ⁻¹ ). The corresponding current-stage-only model yielded R ² = 0.760. Paired bootstrap analysis indicated a cumulative-versus-current-stage improvement of ΔR ² = 0.143 (95% CI, 0.059–0.299) and an RMSE reduction of 14.69 kg mu ⁻¹ (95% CI, 7.28–22.02 kg mu ⁻¹ ). Performance remained similar at mid grain filling (R ² = 0.900), and the early-versus-mid difference was small (ΔR ² = 0.003; 95% CI, −0.015 to 0.025). A nested grouped sensitivity analysis for endpoint-pooling and regularization yielded R ² = 0.896. Early grain filling occurred approximately 27 d before harvest. These results show that multitemporal structural–spectral information can improve pre-maturity winter wheat yield prediction, while the identified prediction window should be interpreted within the present one-site, one-season experiment.",
  "abstract_shown": true,
  "affiliations": [
    "Agricultural Information Institute, Chinese Academy of Agricultural Sciences",
    "Institute of Agricultural Information and Economics, Hebei Academy of Agriculture and Forestry Sciences"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Haodong Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yeping Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Shengping Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Fenghuan Du"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yihan Jiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Run Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Cunchao Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Shijuan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Huili Yue"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cdhehhp9cnsc7.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1953299",
  "fulltext_url": "https://europepmc.org/articles/PMC13713460",
  "id": "cdhehhp9cnsc7",
  "issns": [
    "1664-462X"
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  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1953299/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",
      "2303|Remote Sensing in Agriculture"
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  "openalex": "W7221817022",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1953299/pdf",
  "pmcid": "PMC13713460",
  "pmid": "42952766",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/cdhehhp9cnsc7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1953299",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1953299/full"
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    {
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      "url": "https://europepmc.org/article/MED/42952766"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Winter wheat yield prediction using temporal fusion of multispectral imagery and LiDAR point clouds acquired from a ground-based phenotyping platform",
  "title_text": "Winter wheat yield prediction using temporal fusion of multispectral imagery and LiDAR point clouds acquired from a ground-based phenotyping platform",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:07Z",
  "url": "https://research.draconitic.com/paper/cdhehhp9cnsc7",
  "venue": "Frontiers in Plant Science",
  "versions": []
}