{
  "abstract": "Scalable grapevine physiological assessment is relevant for precision viticulture, but direct physiological measurements are labor-intensive and leaf-specific. This study evaluated whether a machine-learning framework developed for leaf-level hyperspectral measurements can be applied to UAV-derived multispectral imagery. In situ hyperspectral observations from 2024 and 2025 were combined and restricted to the visible and near-infrared (VNIR) range from 350 to 1000 nm to align with common UAV spectral coverage and assess multi-temporal performance and spectral resampling. UAV-derived multispectral observations from 2025 were analyzed for sunlit, shaded, and combined canopy extraction strategies. First-derivative preprocessing improved in situ performance, yielding best $$\\textrm{R}^{2}$$ values of 0.811 for net assimilation rate $$ A $$ , 0.792 for electron transport rate ETR, and 0.890 for effective quantum yield of photosystem II $$ \\Phi _{\\text {PSII}} $$ . Moderate spectral resampling caused limited performance losses for $$ A $$ and ETR. UAV-based models yielded $$\\textrm{R}^{2}$$ up to 0.765 for $$ A $$ , 0.768 for ETR, and 0.901 for $$ \\Phi _{\\text {PSII}} $$ . Shaded and combined observations improved $$ A $$ and ETR predictions relative to sunlit observations, whereas $$ \\Phi _{\\text {PSII}} $$ remained robust across extraction strategies. Plant-grouped validation supported generalization to unseen vines, whereas leave-one-date-out validation failed to demonstrate robust generalization to unseen measurement dates, highlighting temporal generalization as a key limitation of the UAV-based approach.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Marco Lutz"
    },
    {
      "name": "Daniel Heßdörfer"
    },
    {
      "name": "Tobias Ullmann"
    },
    {
      "name": "Melanie Brandmeier"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/awy123vqage3r.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76877-z",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-76877-z",
  "id": "awy123vqage3r",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76877-z",
  "language": "en",
  "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": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7223006099",
  "pdf_url": "https://www.nature.com/articles/s41598-026-76877-z.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/awy123vqage3r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "10.1038/s41598-026-76877-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76877-z"
    },
    {
      "first_seen": "2026-10-30T13:45:56Z",
      "native_id": "0e9a16bb9dda4eb9b573188245e30f59",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-76877-z"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Assessing the feasibility of up-scaling grapevine physiological trait estimation using multispectral UAV imagery",
  "title_text": "Assessing the feasibility of up-scaling grapevine physiological trait estimation using multispectral UAV imagery",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:44Z",
  "url": "https://research.draconitic.com/paper/awy123vqage3r",
  "venue": "Scientific Reports",
  "versions": []
}