{
  "abstract": "Field-deployable plant-health vision systems require datasets that preserve natural variation in illumination, scale, occlusion, and background clutter. This Data Report describes a fixed public release of 500 RGB JPEG images of cucumber leaves collected at three protected-cultivation facilities in Kazakhstan. The collection contains 250 healthy-leaf images and 250 affected-leaf images. Decoder-level inspection confirmed three native image dimensions: 3840 × 2160 pixels (387 images), 4608 × 3456 (112), and 4000 × 2252 (one). The affected subset is paired with 250 text files containing 2,705 rectangular annotations in normalized YOLO center-coordinate format: 1,741 class-0 boxes identify visible non-green color and 964 class-1 boxes identify visible tissue damage. Class 1 includes visible tissue deterioration regardless of whether the apparent cause is disease-associated, mechanical, pest-related, senescent, or otherwise uncertain. The two phenotype labels are operationally non-exclusive: 215 affected images contain both classes, and 116 contain at least one positive-area cross-class box overlap. These labels describe observable phenotypes and do not provide pathogen, disease-species, cultivar, or causal diagnoses. The release supplies bounding boxes, not pixel-level masks; it therefore supports image classification, object detection, and rectangular region localization, but not conventional supervised segmentation. A complete decoder, structure, coordinate, exact-hash, filename, and coarse perceptual-hash audit confirmed one-to-one affected image-label pairing and valid five-field rows, while identifying 42 boundary-crossing boxes in 36 files, 19 byte-identical pairs, and three additional high-similarity candidate pairs. The intentionally balanced final folders are a curated sample and must not be interpreted as natural prevalence. The dataset is publicly available in Mendeley Data, version 1, under CC BY 4.0 at https://doi.org/10.17632/mb3vnhpk8z.1 .",
  "abstract_shown": true,
  "affiliations": [
    "Joldasbekov Institute of Mechanics and Engineering , ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Batyrkhan Omarov"
    },
    {
      "affs": [
        0
      ],
      "name": "Sayat Ibrayev"
    },
    {
      "affs": [
        0
      ],
      "name": "Nurbibi Imanbayeva"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a18944p51g9w0.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1962647",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673411/?tool=EBI",
  "id": "a18944p51g9w0",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13673411",
  "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": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7215986473",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673411/pdf/?tool=EBI",
  "pmcid": "PMC13673411",
  "pmid": null,
  "published": "2026-10-02",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/a18944p51g9w0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:18Z",
      "native_id": "PMC:PMC13673411",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13673411"
    },
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1962647",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1962647/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "An annotated cucumber leaf image dataset for detection of visible damage and abnormal color symptoms in complex cultivation environments",
  "title_text": "An annotated cucumber leaf image dataset for detection of visible damage and abnormal color symptoms in complex cultivation environments",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/a18944p51g9w0",
  "venue": "Frontiers in plant science",
  "versions": []
}