{
  "abstract": "Thrips on sticky traps are extremely small and densely distributed, making automated detection challenging. This study proposes YOLOv11n-WNE, an improved YOLOv11n-based detector tailored for thrips detection in greenhouse environments. Three modifications are introduced: (1) a Wavelet Unpooling StarFusion (WSF) network in the neck, combining WaveletUnPool upsampling—which uses the discrete wavelet transform to preserve spatial details—with StarFusion feature enhancement that captures high-order multi-scale interactions through element-wise multiplication; (2) a Normalized Wasserstein Distance (NWD) loss that models bounding boxes as 2-D Gaussian distributions, providing smooth gradient responses for tiny-object localization; and (3) an EfficientHead detection head using grouped convolutions (G = 8) to compress parameters and computation. A dataset of 2, 400 images with 40, 480 annotated thrips instances was constructed for training (1, 680), validation (360), and testing (360). YOLOv11n-WNE achieves mAP@0.5 of 91.5%, with precision of 88.2% and recall of 85.0%—improvements of 4.1, 4.5, and 5.1 percentage points over the baseline YOLOv11n, respectively—while reducing parameters to 2.11M (81.8% of baseline) and model size to 4.5 MB. On the GPU, the model runs at 123.8 FPS at an input size of 640×640, supporting its deployment for edge-based intelligent thrips monitoring in greenhouses. Beyond individual novelty, the contribution lies in problem-driven integration. WSF, NWD, and EfficientHead act on complementary stages of the detection pipeline—feature aggregation, localization supervision, and model compression, respectively. Together they address the failure modes of thrips-on-sticky-trap imagery: dense tiny objects, weak textures, and frequent overlap with non-target insects.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory for Quality and Safety of Agro-products, Xianghu Laboratory, Institute of Plant Protection and Microbiology, Zhejiang Academy of Agricultural Sciences",
    "Key Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences",
    "Hangzhou Ruikun Technology Co., Ltd.",
    "Anji County Agriculture and Rural Affairs Bureau"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shuxing Zhou"
    },
    {
      "affs": [
        1
      ],
      "name": "Yang Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Yuheng Lu"
    },
    {
      "affs": [
        1
      ],
      "name": "Haoyang Liu"
    },
    {
      "affs": [
        3
      ],
      "name": "Jun Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuhan Pan"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaowei Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinming Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhijun Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jun Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wanying Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "Yaobin Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dkk873dyevqw9.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1945602",
  "fulltext_url": null,
  "id": "dkk873dyevqw9",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1945602/full",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/dkk873dyevqw9.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:21:00Z",
      "native_id": "10.3389/fpls.2026.1945602",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1945602/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1664-462X",
      "subfield": "plant-biology"
    }
  ],
  "title": "Thrips detection on sticky traps based on improved YOLOv11n",
  "title_text": "Thrips detection on sticky traps based on improved YOLOv11n",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:57Z",
  "url": "https://research.draconitic.com/paper/dkk873dyevqw9",
  "venue": "Frontiers in Plant Science",
  "versions": []
}