{
  "abstract": "Plant leaf disease image analysis under real-world field conditions remains challenging due to cluttered backgrounds, illumination variation, overlapping leaves, scale changes, and irregular lesion patterns. This study presents a unified RT-DETR–Mask2Former framework for segmentation-guided plant leaf disease detection and classification. In the proposed pipeline, RT-DETR first localizes leaf regions from complex agricultural images, after which Mask2Former generates fine-grained lesion masks within the detected regions. The refined lesion masks are used to isolate disease-affected regions, enabling EfficientNet-B4 to extract disease-focused feature representations for classification. An EMA-normalized adaptive task-weighting procedure was applied with AdamW optimization to modulate the relative gradient scales of the separately supervised modules. Model development and internal evaluation were conducted using leakage-controlled, group-aware PlantVillage (PV) partitions, whereas 4,200 field-acquired images were reserved exclusively for independent external evaluation. On the complete 38-category PV internal test, accuracy and macro-F1 were 98.34% and 97.97%. On the nine-category source-matched subset, accuracy and macro-F1 were 97.43% and 96.77%, versus 84.93% and 80.80% in the field. PV-to-field performance declined from 95.63% to 89.28% for leaf-localization AP@0.5 and from 92.47%/86.00% to 77.90%/63.80% for lesion-positive Dice/IoU. Lesion guidance improved field macro-F1 from 73.97% to 80.80%. The complete pipeline required approximately 285 GFLOPs and 63 ms per image (16 FPS), demonstrating a practical accuracy–efficiency trade-off while retaining a measurable field-domain gap.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology",
    "Department of General Medicine, Saveetha Institute of Medical and Technical Sciences",
    "Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology",
    "Division of Data Science and Artificial Intelligence, Computer Science and Engineering, Chalmers University of Technology",
    "Division of Data Science and Artificial Intelligence, Computer Science and Engineering, University of Gothenburg",
    "Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava",
    "Department of Electronic and Telecommunication, Constanta Maritime University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "C H. Mohan Sai Kumar"
    },
    {
      "affs": [
        1
      ],
      "name": "Golthu Anusha"
    },
    {
      "affs": [
        2
      ],
      "name": "V. Dhilip Kumar"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Oana Geman"
    },
    {
      "affs": [
        5
      ],
      "name": "Roxana Toderean"
    },
    {
      "affs": [
        6
      ],
      "name": "Mihaela Hnatiuc"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eha62qt1fxgay.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1931819",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716792/?tool=EBI",
  "id": "eha62qt1fxgay",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1931819/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",
      "Original Research"
    ],
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      "openalex",
      "1110|Smart Agriculture and AI"
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  "openalex": "W7222256427",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1931819/pdf",
  "pmcid": "PMC13716792",
  "pmid": "42959330",
  "published": "2026-10-15",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/eha62qt1fxgay.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1931819",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1931819/full"
    },
    {
      "first_seen": "2026-10-31T00:20:37Z",
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      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13716792"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "A unified RT-DETR–Mask2Former framework for segmentation-guided plant leaf disease detection and classification in real-world agricultural environments",
  "title_text": "A unified RT-DETR–Mask2Former framework for segmentation-guided plant leaf disease detection and classification in real-world agricultural environments",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:13Z",
  "url": "https://research.draconitic.com/paper/eha62qt1fxgay",
  "venue": "Frontiers in Plant Science",
  "versions": []
}