{
  "abstract": "Reliable leaf-level trait trajectories require identity-consistent tracking across growth, overlap, and appearance change. We introduce a biology-aware association module combining one appearance cue and three biological/geometric cues: spatial continuity, area-growth plausibility, and lifecycle plausibility. On CanolaTrack, adaptive weighting reduces ID switches from 228 to 126 ± 5 (44.7%; per-plant paired p = 1.6 × 10 ⁻⁵ , n = 37) and improves HOTA from 88.17 to 89.69 ± 0.10. Difficulty-stratified analysis shows IDSW reductions across all 12 CanolaTrack strata, averaging 40.0% versus Appearance-only and 45.0% versus LeafTrackNet. External association-only Arabidopsis and KOMATSUNA tests identify clean low-displacement GT-box regimes where centroid matching or Fixed bio-constraints are more reliable than zero-shot adaptive-weight transfer. These results provide an operating-envelope recommendation: adaptive weighting is most useful in dense CanolaTrack-like sequences, whereas Fixed bio-constraints or centroid matching are more reliable in clean low-displacement external regimes—where nearest-centroid assignment already resolves most associations, so that even a predictor retrained on the target domain does not surpass it. The limiting factor is thus primarily the association difficulty of the regime rather than dataset size.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematics and Computer Science, Wuhan Polytechnic University",
    "School of Electrical and Electronic Engineering, Wuhan Polytechnic University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dongze Zhou"
    },
    {
      "affs": [
        1
      ],
      "name": "Cong Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0vr4gfje7qs76.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1881413",
  "fulltext_url": "https://europepmc.org/articles/PMC13713739",
  "id": "0vr4gfje7qs76",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1881413/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",
      "1110|Smart Agriculture and AI"
    ]
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  "openalex": "W7221780154",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1881413/pdf",
  "pmcid": "PMC13713739",
  "pmid": "42952794",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0vr4gfje7qs76.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1881413",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1881413/full"
    },
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42952794",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42952794"
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    {
      "first_seen": "2026-10-31T12:24:14Z",
      "native_id": "PMC13713739",
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      "url": "https://doi.org/10.3389/fpls.2026.1881413"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Biology-aware association for identity-consistent leaf tracking in top-down plant phenotyping",
  "title_text": "Biology-aware association for identity-consistent leaf tracking in top-down plant phenotyping",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:21Z",
  "url": "https://research.draconitic.com/paper/0vr4gfje7qs76",
  "venue": "Frontiers in Plant Science",
  "versions": []
}