{
  "abstract": "The transition toward industrialized and automated agriculture requires reliable machine vision for high-throughput crop health monitoring and field decision support. Automated sweetpotato disease inspection, however, remains challenging because field images exhibit substantial variations in leaf scale, appearance, illumination, occlusion, and background complexity. Conventional U-shaped networks rely primarily on fixed convolutional kernels and local feature aggregation, whereas Transformer-based alternatives often introduce considerable computational overhead. This article proposes FDUNet-LRSA, a frequency-adaptive segmentation network that integrates frequency-domain dynamic convolution, low-resolution self-attention, and boundary-aware refinement. Frequency-domain dynamic convolution reconstructs input-dependent spatial kernels to improve feature adaptation under changing imaging conditions. Low-resolution self-attention captures long-range contextual dependencies in a fixed compact feature space, while boundary-aware refinement preserves irregular leaf contours during mask reconstruction. Under the reported evaluation protocol, FDUNet-LRSA achieved a mean test foreground IoU of 0.94 and a Dice coefficient of 0.97, with 3.72 GFLOPs. Additional experiments indicate that task-oriented moderate augmentation improves generalization compared with stronger augmentation. The proposed network provides an efficient visual perception approach for automated crop inspection systems.",
  "abstract_shown": true,
  "affiliations": [
    "College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University",
    "School of Engineering and Physical Sciences, University of Lincoln",
    "Lincoln Institute for Agri-food Technology, University of Lincoln",
    "Guangxi Artificial Intelligence Academic and Guilin University of Electronic Technology Nanning Research Institute",
    "School of Electrical Engineering and Automation, Jiangsu Normal University",
    "Key Laboratory of Sweetpotato Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Xuzhou Institute of Agricultural Sciences in Jiangsu Xuhuai District"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ru Han"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Lei Shu"
    },
    {
      "affs": [
        2
      ],
      "name": "Grzegorz Cielniak"
    },
    {
      "affs": [
        3
      ],
      "name": "Yan Zhu"
    },
    {
      "affs": [
        4
      ],
      "name": "Jucheng Yang"
    },
    {
      "affs": [
        5
      ],
      "name": "Fan Yang"
    },
    {
      "affs": [
        5
      ],
      "name": "Weihan Song"
    },
    {
      "affs": [
        3
      ],
      "name": "Qian Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "Kai Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9r2hf7rpcnyz7.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1963106",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719552/?tool=EBI",
  "id": "9r2hf7rpcnyz7",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1963106/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": "W7222502318",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1963106/pdf",
  "pmcid": "PMC13719552",
  "pmid": "42964147",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9r2hf7rpcnyz7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1963106",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1963106/full"
    },
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719552",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719552"
    },
    {
      "first_seen": "2026-11-02T12:23:29Z",
      "native_id": "PMC13719552",
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      "url": "https://doi.org/10.3389/fpls.2026.1963106"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "FDUNet-LRSA: efficient frequency-adaptive segmentation for automated crop disease inspection",
  "title_text": "FDUNet-LRSA: efficient frequency-adaptive segmentation for automated crop disease inspection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:37Z",
  "url": "https://research.draconitic.com/paper/9r2hf7rpcnyz7",
  "venue": "Frontiers in Plant Science",
  "versions": []
}