{
  "abstract": "Accurate detection of rice leaf diseases is critical for intelligent crop protection, yet class imbalance, degradation of multi-scale feature information, and the limited discriminative capability of feature fusion continue to hinder effective monitoring under field conditions. To address these challenges, this study presents AGC-YOLO, an improved detection model based on the YOLOv8s framework. First, a semantic-aware data augmentation network termed SAICP Net is devised to alleviate class imbalance and the scarcity of cross-class co-occurrence samples through a weakly supervised soft spatial prior and semantically constrained cross-category instance pasting. Second, a gated hybrid pooling module named GHP-SPPF is proposed to adaptively integrate max pooling and soft pooling via a learnable gating mechanism, thereby strengthening the discriminative representation of multi-scale lesions. Finally, a channel-adaptive fusion module termed CAFF is constructed to replace standard concatenation with channel-wise learnable weights and gated residual refinement, boosting the discriminative power of cross-scale feature fusion. Experimental results demonstrate that AGC-YOLO achieves an mAP@0.5 of 93.5 ± 0.6% on the rice disease dataset, representing a 3.0 percentage-point improvement over the baseline, and additional experiments on two public datasets further demonstrate the effectiveness of the proposed method for disease detection.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Statistics and Data Science, Southern University of Science and Technology",
    "Department of Basic Education, Wuhu Vocational Technical University",
    "Anhui Longding Information Technology Co., Ltd",
    "School of Network Engineering, Wuhu Vocational Technical University",
    "School of Physics and Information Technology, Shaanxi Normal University"
  ],
  "article_type": "methods article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fei Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuan Lv"
    },
    {
      "affs": [
        2
      ],
      "name": "Li Zhao"
    },
    {
      "affs": [
        3
      ],
      "name": "Jun Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yang Wen"
    },
    {
      "affs": [
        2
      ],
      "name": "Guo Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Xianyao Zhang"
    },
    {
      "affs": [
        4
      ],
      "name": "Yuxiang Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a4sp9pxdebcw6.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1978719",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13725817/?tool=EBI",
  "id": "a4sp9pxdebcw6",
  "issns": [
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  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1978719/full",
  "language": "eng",
  "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": [
    [
      "heading",
      "Methods"
    ],
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      "openalex",
      "1110|Smart Agriculture and AI"
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  "openalex": "W7221945211",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1978719/pdf",
  "pmcid": "PMC13725817",
  "pmid": "42974290",
  "published": "2026-10-15",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/a4sp9pxdebcw6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1978719",
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      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1978719/full"
    },
    {
      "first_seen": "2026-11-03T15:20:51Z",
      "native_id": "PMC:PMC13725817",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13725817"
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    {
      "first_seen": "2026-11-04T15:20:49Z",
      "native_id": "MED:42974290",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42974290"
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    {
      "first_seen": "2026-11-05T12:22:16Z",
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      "url": "https://doi.org/10.3389/fpls.2026.1978719"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "AGC-YOLO: adaptive gated channel network for rice leaf disease detection",
  "title_text": "AGC-YOLO: adaptive gated channel network for rice leaf disease detection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:22:16Z",
  "url": "https://research.draconitic.com/paper/a4sp9pxdebcw6",
  "venue": "Frontiers in Plant Science",
  "versions": []
}