{
  "abstract": "Plant diseases pose a critical challenge to agricultural productivity, underscoring the need for rapid and reliable automated detection methods. Manual disease identification is labor-intensive and error-prone, whereas deep learning techniques offer scalable alternatives. This study evaluates five convolutional neural network (CNN) architectures—XceptionNet, DenseNet121, a baseline five-layer CNN, a six-layer CNN, and a proposed custom architecture, TomaNet-6H—for classifying tomato leaf conditions using the PlantVillage dataset. Image preprocessing combines color space transformations and Contrast Limited Adaptive Histogram Equalization (CLAHE), alongside targeted data augmentation to mitigate class imbalance. Experimental results demonstrate that TomaNet-6H achieves a test accuracy of 97.42% and a macro F1-score of 96.60%, while maintaining moderate computational complexity (∼7M parameters) and an inference latency of 68.4 ms per image. Furthermore, Grad-CAM visualizations qualitatively indicate that the network predominantly focuses on symptomatic leaf areas rather than background artifacts. These findings highlight TomaNet-6H as an effective, resource-conscious framework for precision agriculture deployment.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Information Technology, Bharati Vidyapeeth’s College of Engineering",
    "Information Technology Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)",
    "University Centre for Research and Development (UCRD), Chandigarh University",
    "Galgotias Multi-Disciplinary Research & Development Cell (G-MRDC), Galgotias University",
    "College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)",
    "Department of Computer Science and Information, Applied College, Taibah University",
    "Department of Intelligent Systems, KTH Royal Institute of Technology",
    "Faculty of Computing and IT (FCIT), Sohar University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shubham Roy"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Shakir Khan"
    },
    {
      "affs": [
        0
      ],
      "name": "Neha Gupta"
    },
    {
      "affs": [
        0
      ],
      "name": "Achin Jain"
    },
    {
      "affs": [
        3
      ],
      "name": "Arvind Panwar"
    },
    {
      "affs": [
        4
      ],
      "name": "Fahad Omar Alomary"
    },
    {
      "affs": [
        5
      ],
      "name": "Abeer Aljohani"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Saqib Qamar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eja2yeepseahv.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1970111",
  "fulltext_url": null,
  "id": "eja2yeepseahv",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1970111/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": "W7223358986",
  "pdf_url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2026.1970111/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/eja2yeepseahv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1970111",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1970111/full"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "TomaNet-6H: towards transparent plant disease detection using a modified CNN with Grad-CAM for tomato leaves",
  "title_text": "TomaNet-6H: towards transparent plant disease detection using a modified CNN with Grad-CAM for tomato leaves",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/eja2yeepseahv",
  "venue": "Frontiers in Plant Science",
  "versions": []
}