{
  "abstract": "Automated plant disease classification can benefit from models that provide both predictive performance and interpretable evidence for their decisions. This study presents AgroXAI, a Multi-XAI deep learning framework for explainable multi-species plant disease classification under a controlled, leakage-aware evaluation protocol. A publicly available dataset containing 2,815 leaf images from seven plant species was organized into 14 joint species–health classes and partitioned into fixed group-disjoint training, validation, and held-out test sets containing 1,970, 422, and 423 images, respectively. Preprocessing and architecture selection were conducted using only the training and validation partitions, leading to the AgroX-Swin classifier based on Swin Transformer-Tiny with 384 × 384 Original RGB inputs. Explainability was quantitatively evaluated using Grad-CAM++, SHAP, LIME, and their complementary fusions on a predefined class-balanced subset of 42 held-out images. Across five independently initialized training runs, AgroX-Swin achieved a mean held-out accuracy of 87.19% and macro-F1 of 0.8300 ± 0.0122, while the five-model probability ensemble achieved 87.94% accuracy and 0.8355 macro-F1. Deterministic aggregation of the same 14-class predictions yielded 99.29% species-recognition accuracy and 88.42% binary health-status accuracy, with a diseased-class F1-score of 0.9160. Temperature scaling reduced the representative model's expected calibration error from 0.0792 to 0.0310. At the 20% perturbation level, Grad-CAM++ + LIME produced the largest mean confidence drop (0.2540), while Grad-CAM++ showed the highest transformation stability (0.8371) and SHAP + LIME showed the highest attribution-burden stability (0.7954), demonstrating complementary rather than uniformly superior behavior among individual and fused explanation methods. Model-randomization analysis further examined the dependence of Grad-CAM++ explanations on learned model parameters. AgroXAI provides an integrated framework combining leakage-aware model development, multi-seed training-stability assessment, held-out classification, calibration, statistical uncertainty analysis, and quantitative evaluation of complementary XAI methods. The resulting attribution maps are interpreted as prediction-relevant visual explanations rather than validated lesion localization or quantitative disease-severity estimates. External robustness and real-world agricultural generalization remain to be established using independently acquired field datasets.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Engineering, Vellore Institute of Technology"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sathish Kumar K."
    },
    {
      "affs": [
        0
      ],
      "name": "G. N. Balaji"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6yzmxz12hjjh9.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1965848",
  "fulltext_url": "https://europepmc.org/articles/PMC13713886",
  "id": "6yzmxz12hjjh9",
  "issns": [
    "1664-462X"
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  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1965848/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": "W7221828267",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1965848/pdf",
  "pmcid": "PMC13713886",
  "pmid": "42953232",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/6yzmxz12hjjh9.ris",
  "sources": [
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      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1965848",
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      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1965848/full"
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    {
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      "url": "https://doi.org/10.3389/fpls.2026.1965848"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "AgroXAI: a multi-XAI deep learning system for explainable multi-species plant disease classification",
  "title_text": "AgroXAI: a multi-XAI deep learning system for explainable multi-species plant disease classification",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:29Z",
  "url": "https://research.draconitic.com/paper/6yzmxz12hjjh9",
  "venue": "Frontiers in Plant Science",
  "versions": []
}