{
  "abstract": "UAV-based multi-view reconstruction is an important approach for high-precision, non-destructive 3D crop phenotyping. However, in greenhouse environments, UAV image acquisition is often restricted to sparse viewpoints because of UAV-induced airflow disturbances and the structural complexity of the greenhouse, which severely hinders accurate 3D phenotyping. To address this challenge, this study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments, integrating a vision-triggered flight planning strategy with an improved sparse-view 3DGS pipeline, termed SparseBerry-3DGS, for multi-view image acquisition and 3D phenotyping of greenhouse strawberries in GNSS-denied environments. Specifically, 3D Gaussian Splatting (3DGS) is improved by incorporating flow-guided initialization, depth supervision, and an adaptive pruning strategy, which effectively alleviate geometric collapse and floating artifacts under sparse-view conditions. Furthermore, sequential semantic masks generated by SAM2 are utilized to guide the segmentation of strawberry point clouds, thereby reducing background interference and segmentation errors. Experimental results show that the vision-triggered flight strategy enables stable capture of 16 surrounding images for each target fruit. Under sparse-view conditions, SparseBerry-3DGS improves reconstruction stability, with the average peak signal-to-noise ratio (PSNR) reaching 18.25 dB, corresponding to an 18% improvement. The SAM2-based segmentation module achieves high accuracy, with the mean intersection over union (mIoU) above 0.95. Geometric evaluation based on strawberry longitudinal diameter yielded an R2 of 0.88, supporting the accuracy of fruit-scale geometric reconstruction. For weight estimation, five-fold cross-validation yielded an R2 of 0.90 and an RMSE of 3.62 g, showing better predictive performance than models based on 2D projected area and standard 3DGS point clouds. This study provides a new approach for high-throughput, non-invasive digital crop phenotyping in greenhouse horticulture.",
  "abstract_shown": true,
  "affiliations": [
    "College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China",
    "School of Information Engineering, Huzhou University, Huzhou, 313000, China",
    "Institute of Biotechnology Research, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, China",
    "College of Information Science and Technology, Shihezi University, Shihezi, 832003, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiayi Cai"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhenyu Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuzhen Wei"
    },
    {
      "affs": [
        2
      ],
      "name": "Wenfei Xiao"
    },
    {
      "affs": [
        2
      ],
      "name": "Hong Yu"
    },
    {
      "affs": [
        3
      ],
      "name": "Pan Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Xuping Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yong He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0hp2mev03w3ec.bib",
  "cited_by": 0,
  "doi": "10.1016/j.plaphe.2026.100281",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13718796/?tool=EBI",
  "id": "0hp2mev03w3ec",
  "issns": [
    "2643-6515"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13718796",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
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      "openalex",
      "1110|Smart Agriculture and AI"
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  "openalex": "W7207684777",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13718796/pdf/?tool=EBI",
  "pmcid": "PMC13718796",
  "pmid": "42960280",
  "published": "2026-09-03",
  "publisher": "Nanjing Agricultural University",
  "ris": "https://research.draconitic.com/paper/0hp2mev03w3ec.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:39Z",
      "native_id": "PMC:PMC13718796",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13718796"
    },
    {
      "first_seen": "2026-11-01T00:20:52Z",
      "native_id": "MED:42960280",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42960280"
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    {
      "first_seen": "2026-11-02T12:22:45Z",
      "native_id": "PMC13718796",
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "A UAV-based sparse-view 3DGS framework for greenhouse strawberry reconstruction",
  "title_text": "A UAV-based sparse-view 3DGS framework for greenhouse strawberry reconstruction",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:22:45Z",
  "url": "https://research.draconitic.com/paper/0hp2mev03w3ec",
  "venue": "Plant phenomics (Washington, D.C.)",
  "versions": []
}