{
  "abstract": "Crop diseases threaten agricultural productivity, creating a need for recognition models that combine accuracy, robustness, and computational efficiency. Existing CNN-, Transformer-, and state-space-based approaches can weaken fine-grained lesion edges and textures when emphasizing global context. We therefore propose MambaSeed, a lightweight hybrid network built on MambaVisionMixer and equipped with the Local Continuous Branch Injection mechanism, hereafter LCBI. This mechanism repeatedly introduces local feature compensation during hierarchical modeling, preserving high-frequency cues while maintaining global dependency modeling. On the Tomato, Corn, and Potato datasets, MambaSeed achieved accuracies of 98.97%, 98.06%, and 95.87%, respectively, with 1.13M parameters and 0.240G MACs. In the batch-one FP32 GPU benchmark, its latency was 3.39 ms/image on an NVIDIA RTX 4090. Without a hardware accelerator, the median CPU latency was 25.35 ms/image with one thread and 15.61 ms/image with four threads. Ablation, cross-architecture frequency analysis, and controlled perturbation tests further showed that LCBI strengthens high-frequency feature retention and maintains stable recognition under visual degradation. Together, these results show that MambaSeed provides a compact balance of accuracy, efficiency, and robustness across both accelerator-based and CPU-only inference settings.",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, China",
    "State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi’an University of Technology, Xi’an, China",
    "School of Water Resources and Hydro-electric Engineering of XUT, Xi’an University of Technology, Xi’an, China",
    "Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, Heilongjiang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
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        0
      ],
      "name": "XueShen Wang"
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    {
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        1
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      "name": "Heng Zhang"
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    {
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        2
      ],
      "name": "Yayong Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "YunLong Li"
    },
    {
      "name": "YuJie Qin"
    },
    {
      "name": "ZhongYu Lv"
    },
    {
      "name": "XiangJuan Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f3j4xwpjysdvq.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359350",
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  "id": "f3j4xwpjysdvq",
  "issns": [
    "1932-6203"
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  "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/",
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  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ],
    [
      "plos",
      "/Biology and life sciences/Agriculture/Crop science/Crop diseases"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Fruits/Tomatoes"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Grasses/Maize"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Solanum/Potato"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Vegetables/Potato"
    ],
    [
      "plos",
      "/Biology and life sciences/Plant science/Plant anatomy/Leaves"
    ],
    [
      "plos",
      "/Biology and life sciences/Plant science/Plant pathology"
    ],
    [
      "plos",
      "/Research and analysis methods/Animal studies/Experimental organism systems/Model organisms/Maize"
    ],
    [
      "plos",
      "/Research and analysis methods/Animal studies/Experimental organism systems/Plant and algal models/Maize"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Research and analysis methods/Model organisms/Maize"
    ],
    [
      "plos",
      "/Social sciences/Linguistics/Semantics"
    ]
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  "openalex": "W7222349384",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359350&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/f3j4xwpjysdvq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0359350",
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      "url": "https://doi.org/10.1371/journal.pone.0359350"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:plant science",
      "subfield": "plant-biology"
    }
  ],
  "title": "MambaSeed: A lightweight Mamba-based hybrid vision network for tomato leaf disease recognition",
  "title_text": "MambaSeed: A lightweight Mamba-based hybrid vision network for tomato leaf disease recognition",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/f3j4xwpjysdvq",
  "venue": "PLOS One",
  "versions": []
}