{
  "abstract": "Accurate sowing-quality assessment of maize seedling trays requires both reliable seed localisation and correct correspondence between detected seeds and physical tray cells. This study presents a semi-automated machine-vision framework combining heatmap-based seed-centre localisation, sliding-window inference, global non-maximum suppression (NMS), and four-corner-calibrated adaptive grid assignment. Each assessable cell was classified as empty, single-seeded, or multi-seeded according to the number of assigned seed centres. On source-image-independent subsets, the framework achieved an accuracy of 0.9821 and a macro F1-score of 0.9040 for 27 complete-tray images (3,456 cells), and 0.9798 and 0.9344, respectively, for 9 visible-cell images (1,140 assessable cells). Separately, the larger evaluation sets yielded accuracy/macro F1 values of 0.9783/0.9145 for 44 complete trays and 0.9782/0.9368 for 30 visible-cell images. These results support cell-level sowing-quality assessment under the evaluated acquisition conditions and within manually assessable regions. The source-image-independent subsets control direct source overlap but remain within the same acquisition domain; external generalisation across production sites, imaging devices, tray specifications, and acquisition conditions has not yet been established.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Software Technologies, Shanxi Agricultural University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiangchen Zan"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinfeng Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Ruijun Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiangyu Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Bowen Xue"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoying Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0g1m0jh4kg229.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1981493",
  "fulltext_url": "https://europepmc.org/articles/PMC13739767",
  "id": "0g1m0jh4kg229",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1981493/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": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7223327648",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1981493/pdf",
  "pmcid": "PMC13739767",
  "pmid": "42996349",
  "published": "2026-10-23",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/0g1m0jh4kg229.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1981493",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1981493/full"
    },
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42996349",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42996349"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "From seed centres to tray cells: a geometry-aware machine vision framework for full-tray sowing quality assessment in maize plug trays",
  "title_text": "From seed centres to tray cells: a geometry-aware machine vision framework for full-tray sowing quality assessment in maize plug trays",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/0g1m0jh4kg229",
  "venue": "Frontiers in Plant Science",
  "versions": []
}