{
  "abstract": "In precision agriculture, assessing missing seedlings is essential for variable-rate reseeding and yield estimation. However, missing plants represent a semantic absence rather than explicit visual targets, making them difficult to define using conventional bounding-box annotations. To address this challenge, we propose a video-based, detection-driven gap inference framework for UAV wheat seedling phenotyping. Specifically, a semi-supervised Dual-Stream Anchor-Refinement Tracking (DART) mechanism is introduced to generate reliable pseudo-labels for visible seedlings from sparse temporal anchors, reducing dependence on dense manual annotations. To address visual fragmentation and weak target responses, a Masked Trajectory Inpainting Network (MTI-Net) combines object-aware masking with history-guided feature reconstruction to recover incomplete seedling representations using temporal context. The refined visible-seedling detections are then converted into agronomic gap indicators through a Dual-Scale Inertial Metric (DS-IM), which incorporates mechanical-sowing kinematic regularities and local growth-space constraints. Evaluations on the UAV-WheatSeedling dataset demonstrate positive mean gains when the proposed modules are embedded into RT-DETR and multiple YOLO-family backbones. When integrated with YOLO12-S, the method achieves 92.92 ± 0.17% mAP@0.50, 69.33 ± 0.22% mAP@[0.5:0.95], and 86.59 ± 0.41% Recall. Under 50% sparse-anchor supervision, the proposed framework approaches the 100% sparse-anchor YOLO12 baseline in mAP@[0.5:0.95], while achieving higher mean Recall. For downstream gap inference, DS-IM achieves an F1-score of 84.53% and a frame-level count MAE of 0.229 gaps/frame. This study provides a data-efficient and interpretable framework for low-altitude UAV-based early seedling assessment.",
  "abstract_shown": true,
  "affiliations": [
    "School of Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China",
    "Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China",
    "College of Electronics and Electrical Engineering, Anhui Agricultural University, Hefei, 230036, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yaoyao Nie"
    },
    {
      "affs": [
        0
      ],
      "name": "Huimin Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoshen Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Pengfei Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yi Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaohong Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Lichuan Gu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ctx5n5fh9jr3t.bib",
  "cited_by": 0,
  "doi": "10.1016/j.plaphe.2026.100278",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13720541/?tool=EBI",
  "id": "ctx5n5fh9jr3t",
  "issns": [
    "2643-6515"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13720541",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7211932224",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13720541/pdf/?tool=EBI",
  "pmcid": "PMC13720541",
  "pmid": "42964727",
  "published": "2026-09-08",
  "publisher": "Nanjing Agricultural University",
  "ris": "https://research.draconitic.com/paper/ctx5n5fh9jr3t.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:37Z",
      "native_id": "PMC:PMC13720541",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13720541"
    },
    {
      "first_seen": "2026-11-03T12:21:24Z",
      "native_id": "PMC13720541",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.plaphe.2026.100278"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Label-efficient seedling perception via semi-supervised learning and counterfactual spatiotemporal feature rectification",
  "title_text": "Label-efficient seedling perception via semi-supervised learning and counterfactual spatiotemporal feature rectification",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:24Z",
  "url": "https://research.draconitic.com/paper/ctx5n5fh9jr3t",
  "venue": "Plant phenomics (Washington, D.C.)",
  "versions": []
}