{
  "abstract": "Introduction: Meteorological variability influences diamondback moth (DBM; Plutella xylostella) infestation and the resulting damage to Brassica crops. Accurate multi-horizon forecasting requires models that preserve short-lived meteorological exposures while integrating thermal timing, spatial dependence, and recent infestation dynamics within a transparent framework.\n\nMethods: The Temporal–Geospatial Pest Network (TG-PestNet) forecasts the weekly percentage of inspected plants classified as DBM-infested 1 to 4 weeks ahead at four Guangdong stations, from origin-time information only. It preserves hourly exposure, encodes lookback weeks on a thermal-time axis, couples stations through a wind-informed graph, and protects infestation history in a gated anchor. Evaluation used 344 synchronized weeks, 66 later test origins, 13 history-only comparators, retraining ablations, matched-width controls, and input-matched generic encoders.\n\nResults and discussion: TG-PestNet achieved the lowest observed root-mean-square error, mean absolute error, and mean absolute percentage error in all 12 station–metric combinations, with mean reductions of 5.9%–13.0%. Year-ahead forecasts outperformed persistence in all four test years. Removing the history anchor increased error by 14.3% and remained significant after multiplicity correction, demonstrating its central contribution to forecast accuracy. Meteorological information provided complementary predictive value: its occlusion increased the error by 9.3%, and retraining without it increased the macro RMSE by 3.6%. TG-PestNet also substantially outperformed the two input-matched generic encoders, whose errors were approximately one quarter higher. These results establish TG-PestNet as an accurate, reproducible and auditable framework for multi-station pest forecasting, with component-level analyses that identify the dominant contribution of recent infestation history and characterize the predictive roles of high-resolution meteorology and structured spatial information.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information Management, Sun Yat-sen University, Guangzhou, Guangdong, China",
    "Institute of Quality Standard and Monitoring Technology for Agro-products of Guangdong Academy of Agricultural Sciences, Guangzhou, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dong Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiale Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9wa3xsnsxqff1.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1953590",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13696400/",
  "id": "9wa3xsnsxqff1",
  "issns": [
    "1664-462X"
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  "landing_url": "https://doi.org/10.3389/fpls.2026.1953590",
  "language": "en",
  "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": [
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      "heading",
      "Original Research"
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      "openalex",
      "1110|Smart Agriculture and AI"
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  "openalex": "W7221510543",
  "pdf_url": "https://europepmc.org/articles/PMC13696400?pdf=render",
  "pmcid": "PMC13696400",
  "pmid": "42935564",
  "published": "2026-10-12",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9wa3xsnsxqff1.ris",
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    {
      "first_seen": "2026-10-30T12:17:45Z",
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Multi-source temporal fusion and multi-station learning for early warning of diamondback moth infestation in Brassica crops",
  "title_text": "Multi-source temporal fusion and multi-station learning for early warning of diamondback moth infestation in Brassica crops",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:33:44Z",
  "url": "https://research.draconitic.com/paper/9wa3xsnsxqff1",
  "venue": "Frontiers in Plant Science",
  "versions": []
}