{
  "abstract": "Introduction: Plant diseases pose a major threat to food-supply sustainability. Early identification of crop diseases is crucial for agricultural production and crop yield. Classifying tomato leaves helps determine the treatments needed to keep crops healthy. Although many deep learning approaches have been proposed for plant disease classification, most rely on laboratory-captured images and struggle with real-field conditions.\n\nMethods: This study introduces an extensive class-based dataset collected in natural environments, visually distinguishing 13 infectious diseases and healthy tomato leaves. We propose the Gamma-preprocessed Squeeze-Excitation Block Network (GSEBNet), a lightweight CNN architecture that integrates Squeeze-and-Excitation (SE) blocks with carefully configured Mobile Inverted Bottleneck Convolution (MBConv) layers and a domain-specific Gamma-Unsharp Brightness Reduction (GUBR) preprocessing pipeline. The model was trained and evaluated on a combined dataset of field-captured (PlantCity) and laboratory (PlantVillage) images covering 14 classes.\n\nResults: The proposed GSEBNet achieved a test accuracy of 99.48% on the 14-class dataset, with precision, recall, F1-score, and Cohen’s Kappa of 99.49%, 99.49%, 0.99, and 0.9944, respectively.\n\nDiscussion: The obtained results are competitive with those reported in previous studies, while acknowledging differences in datasets and experimental settings. The synergistic co-design of GUBR preprocessing with SE-enhanced MBConv layers significantly improves feature discriminability in low-quality field-captured images.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Islamia College University",
    "Quantum Technologies and Advanced Computing Institute, King Abdulaziz City for Science and Technology",
    "Department of Electrical Engineering, College of Engineering, Qassim University",
    "Department of Information Technology, College of Computer, Qassim University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammad Sheraz Khan"
    },
    {
      "affs": [
        0
      ],
      "name": "Irshad Ahmad"
    },
    {
      "affs": [
        0
      ],
      "name": "Kainat Nisa"
    },
    {
      "affs": [
        1
      ],
      "name": "Faisal Alharbi"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammed Aloraini"
    },
    {
      "affs": [
        3
      ],
      "name": "Shabana Habib"
    },
    {
      "affs": [
        2
      ],
      "name": "Muhammad Islam"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1n0bjcaamqmqg.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1947340",
  "fulltext_url": null,
  "id": "1n0bjcaamqmqg",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1947340/full",
  "language": null,
  "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": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7224128674",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/1n0bjcaamqmqg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1947340",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1947340/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Enhancing crop sustainability: GSEBNet for precise tomato leaf disease classification",
  "title_text": "Enhancing crop sustainability: GSEBNet for precise tomato leaf disease classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/1n0bjcaamqmqg",
  "venue": "Frontiers in Plant Science",
  "versions": []
}