{
  "abstract": "Pathogen-induced disease causes heavy post-harvest losses and threatens food safety, yet routine inspection rarely identifies an infection before it becomes visible. This study evaluates multimodal sensing, threshold-based early warning and plasma-based decontamination for post-harvest disease management in strawberry (Fragaria × ananassa cv. ‘Hongyan’). A dual-branch network, pairing a ResNet-34 visual encoder with a one-dimensional convolutional spectral encoder through bidirectional cross-modal attention, fuses RGB imagery with near-infrared hyperspectral reflectance. Across five severity grades the fused model reached 93.5 ± 0.7% accuracy over fifteen cross-validation runs and 94.2% on a fruit-level held-out test set of 260 samples (95% CI 91.4 to 97.1), with a weighted F1 score of 0.938 and a Cohen’s Kappa of 0.927. Its 2.7-point margin over an unweighted late-fusion baseline did not reach significance after correction for eleven held-out comparisons (continuity-corrected McNemar $$\\:{\\chi\\:}^{2}$$ = 4.00, Holm-adjusted $$\\:p$$ = 0.140). Alarms preceded the expert-confirmed transition to a visible lesion by 52.0 ± 9.6 h in the 80-fruit monitoring cohort. Cold atmospheric plasma parameters were screened systematically: 1.5 W/cm ² in Ar/O ₂ for 180 s delivered more than a 3.5-log reduction for Botrytis cinerea , Colletotrichum gloeosporioides and Aspergillus niger ; the measured quality attributes did not differ significantly from untreated controls under the tested conditions. Leakage assays and a sigmoidal dose-response model support membrane damage associated with oxidative stress. The proposed progression from membrane peroxidation through enzyme disruption to DNA damage remains a mechanistic hypothesis. Sensing and treatment were evaluated separately; the present evidence is limited to one commodity, laboratory conditions and no integrated pilot-scale trial.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "BaoJing Sun"
    },
    {
      "name": "Wei Zhang"
    },
    {
      "name": "XiaoLong Hu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9m2kkccf80b4x.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-75069-z",
  "fulltext_url": null,
  "id": "9m2kkccf80b4x",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-75069-z",
  "language": null,
  "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": "W7221764201",
  "pdf_url": "https://www.nature.com/articles/s41598-026-75069-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/9m2kkccf80b4x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "10.1038/s41598-026-75069-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-75069-z"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Vision-spectroscopy cooperative sensing and cold atmospheric plasma inactivation for early warning and management of agricultural product diseases",
  "title_text": "Vision-spectroscopy cooperative sensing and cold atmospheric plasma inactivation for early warning and management of agricultural product diseases",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:28Z",
  "url": "https://research.draconitic.com/paper/9m2kkccf80b4x",
  "venue": "Scientific Reports",
  "versions": []
}