{
  "abstract": "The rapid advancement of smart agricultural technologies has enabled deep learning–based approaches to markedly enhance the efficiency of crop disease detection and quality monitoring. To overcome the challenges of low detection efficiency and high false-positive rates in large-scale cultivation of the characteristic economic crop Rosa roxburghii, this study introduces a synergistic spatial–sequential modeling framework. This synergy is achieved via a gated fusion mechanism that bidirectionally enhances CNN-extracted local spatial features and Mamba-derived global sequential features through adaptive re-weighting, forming a mutual refinement loop instead of a simple cascade. Built upon YOLOv8, the model integrates the Mamba module from state space models (SSMs) to develop a hybrid feature extraction framework, jointly optimizing detection accuracy and inference efficiency. A selective feature enhancement strategy further strengthens the network’s ability to characterize subtle lesions on the surface of Rosa roxburghii by adaptively amplifying informative responses through channel-wise recalibration and spatial refinement. The backbone network also incorporates the selective scan for 2D data (SS2D) module to capture long-range dependencies beyond conventional convolution. Experimental validation conducted on our self-constructed dataset, which consists of field-captured images of Rosa roxburghii plants manually collected and annotated by the research team, demonstrates that the proposed model delivers notable performance gains, achieving mAP@50 improvements of 6%, 2.87%, 8.38%, 7.08%, 16.27%, and 6.55% over YOLOv5-N, YOLOv8-N, YOLOv10-N, YOLOv11-N, YOLOv12-N, and MambaYOLO-T, respectively. Concerning mean average precision (mAP)@ 50–95, the model attains corresponding gains of 6.85%, 0.27%, 6.99%, 5.19%, 13.66%, and 2.57%, all while maintaining comparable FLOPs (G) and parameter counts. These results highlight the model’s excellent performance, confirming its effectiveness for agricultural visual inspection and suggesting a promising approach for intelligent disease monitoring in mountainous specialty crop production.",
  "abstract_shown": true,
  "affiliations": [
    "Engineering Research Center of Micro-Nano and Intelligent Manufacturing, Ministry of Education, Kaili University, Kaili, Guizhou, China",
    "College of Microelectronics and Artificial Intelligence, Kaili University, Kaili, Guizhou, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xude Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Houbing Tang"
    },
    {
      "name": "Xiaoping Wu"
    },
    {
      "name": "Jianghai Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9v0n41asqt9ne.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358868",
  "fulltext_url": null,
  "id": "9v0n41asqt9ne",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0358868",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ],
    [
      "plos",
      "/Biology and life sciences/Agriculture"
    ],
    [
      "plos",
      "/Biology and life sciences/Agriculture/Crop science/Crops"
    ],
    [
      "plos",
      "/Biology and life sciences/Agriculture/Pests/Insect pests"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Intelligence"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Fruits"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Intelligence"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Intelligence"
    ]
  ],
  "openalex": "W7220705616",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358868&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9v0n41asqt9ne.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:36Z",
      "native_id": "10.1371/journal.pone.0358868",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358868"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Synergistic spatial-sequential modeling for enhanced Rosa roxburghii disease detection",
  "title_text": "Synergistic spatial-sequential modeling for enhanced Rosa roxburghii disease detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:36Z",
  "url": "https://research.draconitic.com/paper/9v0n41asqt9ne",
  "venue": "PLOS One",
  "versions": []
}