{
  "abstract": "IntroductionAccurate maize pest detection is hindered by morphological similarity among pest categories, complex backgrounds, and the loss of spatial detail in small targetsMethodsTo address these challenges, we developed a stage-wise collaborative feature enhancement framework based on RT-DETR-R18. A Similarity-aware Decoupling Module combines orthogonal feature transformation with channel excitation to enhance discriminative backbone features. Adaptive Sparse Self-Attention regulates high-level inter-region interactions, whereas an Efficient Up-Convolution Block recovers spatial detail during top-down feature fusion. The method was evaluated on a primary dataset comprising 2,311 images from four maize pest categories and on the separately constructed IP102-Maize dataset, which was derived from the public IP102 benchmark and comprised 4,533 images from 13 categories.ResultsOn the primary dataset, the proposed method achieved 90.4% mAP@50 and 67.9% mAP@50:95, improving mAP@50 by 2.7 percentage points over RT-DETR-R18. The model contained 20.9 M parameters and required 58.9 GFLOPs. On IP102-Maize, it achieved 64.1% mAP@50, exceeding RT-DETR-R18, RT-DETR-R34, and YOLOv10-S by 2.7, 4.0, and 1.7 percentage points, respectively.DiscussionThese results support the effectiveness of the tested stage-wise enhancement configuration for maize pest detection in two public-dataset settings. Further field and deployment evaluation is required to establish its practical applicability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Jiayi Zuo"
    },
    {
      "name": "Cong Zhang"
    },
    {
      "name": "Donghan Liu"
    },
    {
      "name": "Zheng Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3e9gvya16f1ya.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.2011789",
  "fulltext_url": null,
  "id": "3e9gvya16f1ya",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.2011789",
  "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": [
    [
      "feed-journal",
      "biology/plant-biology"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/3e9gvya16f1ya.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:20:46Z",
      "native_id": "10.3389/fpls.2026.2011789",
      "source": "feed-frontiers",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.2011789"
    },
    {
      "first_seen": "2026-11-02T12:23:28Z",
      "native_id": "10.3389/fpls.2026.2011789",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.2011789/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "feed-journal:biology/plant-biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "Stage-wise collaborative feature enhancement for accurate maize insect pest detection in complex agricultural scenes",
  "title_text": "Stage-wise collaborative feature enhancement for accurate maize insect pest detection in complex agricultural scenes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T00:25:40Z",
  "url": "https://research.draconitic.com/paper/3e9gvya16f1ya",
  "venue": "Frontiers in Plant Science",
  "versions": []
}