{
  "abstract": "Accurate detection of apple fruitlets in complex orchard environments is a prerequisite for precision fruit thinning but remains challenging due to small target sizes, variable illumination, and dense foliage. To address these issues, a lightweight YOLOv12 architecture, termed LT-YO-AN, was proposed. The backbone integrates RepDown re-parameterizable downsampling and D3C2f aggregation modules, enabling hierarchical feature extraction with reduced spatial redundancy and channel-level compression. An asymmetric neck is introduced to explicitly enhance the high-resolution P2 layer for small-target sensitivity, while deliberately weakening the P5 layer via channel reduction (256 to 128) to alleviate redundancy. This design is complemented by a four-scale detection head (P2–P5) and a width scaling strategy (factor = 0.25), which together allocate computational resources preferentially to shallow features, yielding a compact model optimized for apple fruitlet detection. Evaluations on a real-world orchard dataset demonstrate that LT-YO-AN achieves an mAP50–95 of 0.675 and a recall of 0.860, outperforming benchmark YOLO variants. Although the computational complexity increases to 11.5 GFLOPs, the model maintains a compact size of 3.5 MB with 1.49 M parameters, making it suitable for edge deployment. This study provides a viable technological solution for early-stage fruit detection and automated orchard management.",
  "abstract_shown": true,
  "affiliations": [
    "College of Communication and Information Engineering, Xi’an University of Science and Technology",
    "Xi’an Key Laboratory of Network Convergence Communication",
    "School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Dandan Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Bo Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5cj1hs34dbcy4.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1958019",
  "fulltext_url": "https://europepmc.org/articles/PMC13713831",
  "id": "5cj1hs34dbcy4",
  "issns": [
    "1664-462X"
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  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1958019/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "heading",
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      "openalex",
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  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1958019/pdf",
  "pmcid": "PMC13713831",
  "pmid": "42952847",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/5cj1hs34dbcy4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1958019",
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      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1958019/full"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "LT-YO-AN: lightweight YOLOv12 with asymmetric neck for apple fruitlet detection in complex orchard environments",
  "title_text": "LT-YO-AN: lightweight YOLOv12 with asymmetric neck for apple fruitlet detection in complex orchard environments",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:26Z",
  "url": "https://research.draconitic.com/paper/5cj1hs34dbcy4",
  "venue": "Frontiers in Plant Science",
  "versions": []
}