{
  "abstract": "Self-supervised Learning (SSL) has become a powerful paradigm for representation learning without manual annotations. However, most existing frameworks focus on global alignment and struggle to capture the hierarchical, multi-scale lesion patterns characteristic of plant disease imagery. To address this gap, we propose PSMamba , a progressive self-supervised framework that integrates the efficient sequence modelling of Vision Mamba (VM) with a dual-student hierarchical distillation strategy. Unlike conventional single teacher-student designs, PSMamba employs a shared global teacher and two specialised students: one processes mid-scale views to capture lesion distributions and vein structures, while the other focuses on local views to capture fine-grained cues such as texture irregularities and early-stage lesions. This multi-granular supervision facilitates the joint learning of contextual and detailed representations, with consistency losses ensuring coherent cross-scale alignment. Experiments on three benchmark datasets show that PSMamba consistently outperforms representative CNN-, Transformer-, SSL-, and Mamba-based baselines, delivering superior accuracy and robustness in both domain-shifted and fine-grained scenarios.",
  "abstract_shown": true,
  "affiliations": [
    "Imaging and Computer Vision Group, Data61, CSIRO, Black Mountain, 2601, Canberra, Australia",
    "School of Engineering and Built Environment, Griffith University, Nathan, 4111, Queensland, Australia",
    "School of Information and Communication Technology, Griffith University, Nathan, 4111, Queensland, Australia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Abdullah Al Mamun",
      "orcid": "0000-0002-7075-0662"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Miaohua Zhang",
      "orcid": "0000-0002-5281-7851"
    },
    {
      "affs": [
        0
      ],
      "name": "David Ahmedt‐Aristizabal",
      "orcid": "0000-0003-1598-4930"
    },
    {
      "affs": [
        0
      ],
      "name": "Zeeshan Hayder",
      "orcid": "0009-0009-7421-0311"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammad Awrangjeb",
      "orcid": "0000-0002-2711-4329"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7e1efbz41qhwe.bib",
  "cited_by": 9,
  "doi": "10.1016/j.eswa.2026.133775",
  "fulltext_url": null,
  "id": "7e1efbz41qhwe",
  "issns": [
    "0957-4174",
    "1873-6793"
  ],
  "landing_url": "https://doi.org/10.1016/j.eswa.2026.133775",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7171835127",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-30",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/7e1efbz41qhwe.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T13:20:49Z",
      "native_id": "W7171835127",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.eswa.2026.133775"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "PSMamba: Progressive self-supervised vision Mamba for plant disease recognition",
  "title_text": "PSMamba: Progressive self-supervised vision Mamba for plant disease recognition",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:08Z",
  "url": "https://research.draconitic.com/paper/7e1efbz41qhwe",
  "venue": "Expert Systems with Applications",
  "versions": []
}