{
  "abstract": "The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "name": "Zhenming Song"
    },
    {
      "name": "Chen Zhu"
    },
    {
      "name": "Rui Yu"
    },
    {
      "name": "Pingtao Jiang"
    },
    {
      "name": "Yangmingrui Gao"
    },
    {
      "name": "Benoit de Solan"
    },
    {
      "name": "Jianhui Wu"
    },
    {
      "name": "Dejun Han"
    },
    {
      "name": "Yanfeng Ding"
    },
    {
      "name": "Frédéric Baret"
    },
    {
      "name": "Ruixi Han"
    },
    {
      "name": "Shouyang Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0a18mz3h0954v.bib",
  "cited_by": 2,
  "doi": "10.1016/j.plaphe.2026.100192",
  "fulltext_url": null,
  "id": "0a18mz3h0954v",
  "issns": [
    "2643-6515"
  ],
  "landing_url": "https://hal.inrae.fr/hal-05781675v1",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.sa"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7133188309",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Science Partner Journals",
  "ris": "https://research.draconitic.com/paper/0a18mz3h0954v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "hal-05781675",
      "source": "hal",
      "url": "https://hal.inrae.fr/hal-05781675v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Synthetic-augmented multimodal deep learning fuses dual-angle RGB images and phenology to unlock genotype-informative canopy structural trait in wheat",
  "title_text": "Synthetic-augmented multimodal deep learning fuses dual-angle RGB images and phenology to unlock genotype-informative canopy structural trait in wheat",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/0a18mz3h0954v",
  "venue": "Plant Phenomics",
  "versions": []
}