{
  "abstract": "Cucumber foliar symptoms reduce functional leaf area and complicate greenhouse scouting. This study presents CuFormer U-Net, a Transformer-enhanced multi-task network for three-class image-level classification on the 3,754-image Kaggle benchmark (healthy, powdery mildew, and downy mildew) and binary visible-symptom segmentation on a separate 400-image greenhouse cohort (200 healthy and 200 affected leaves). The greenhouse annotations comprise 2,705 rectangles (1,741 color-abnormality and 964 tissue-damage instances); rectangle-seeded lesion masks were manually refined for the dense-prediction experiments. The reported fixed-split point estimates were 96.7% accuracy, 96.3% macro precision, 95.8% macro recall, and 96.0% macro F1 for classification, and 92.4% mean image-wise Dice, 83.1% macro IoU, 93.2% foreground precision, and 91.7% foreground recall for segmentation. The complete internal-validation ablation configuration reached 93.1% Dice and 87.3% IoU. The available experiment archive does not contain independent-seed reruns, per-image predictions for inferential testing, annotator-level masks, quantitative results for the separate 100-image greenhouse stress-test cohort, or reproducible hardware profiling. Accordingly, all between-model differences are interpreted descriptively, the external cohort is not presented as formal external validation, and robustness and deployment claims have been moderated. CuFormer U-Net provides a technically motivated basis for joint recognition and spatial characterization, but confirmatory multi-run and multi-site evaluation is required.\n\nboundary refinement, cucumber leaf disease, multi-task learning, plant disease, precision agriculture, semantic segmentation, vision transformer",
  "abstract_shown": true,
  "affiliations": [
    "Laboratory of Mechanics of Robots and Manipulators, Joldasbekov Institute of Mechanics and Engineering",
    "Department of Mathematical and Computer Modeling, International Information Technology University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Batyrkhan Omarov"
    },
    {
      "affs": [
        0
      ],
      "name": "Sayat Ibrayev"
    },
    {
      "affs": [
        0
      ],
      "name": "Nurbibi Imanbayeva"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/axf2tvcrj57se.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1975030",
  "fulltext_url": "https://europepmc.org/articles/PMC13683789",
  "id": "axf2tvcrj57se",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1975030/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": "W7220838309",
  "pdf_url": "https://europepmc.org/articles/PMC13683789?pdf=render",
  "pmcid": "PMC13683789",
  "pmid": "42909094",
  "published": "2026-10-07",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/axf2tvcrj57se.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1975030",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1975030/full"
    },
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "MED:42909094",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42909094"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "CuFormer U-Net: a transformer-enhanced multi-task network for cucumber leaf disease detection and segmentation",
  "title_text": "CuFormer U-Net: a transformer-enhanced multi-task network for cucumber leaf disease detection and segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/axf2tvcrj57se",
  "venue": "Frontiers in Plant Science",
  "versions": []
}