{
  "abstract": "Maize (Zea mays L.) is a rapidly expanding cereal crop in Bangladesh, but its yield is threatened by biotic diseases, insect pests, and nutrient-deficiency disorders that are difficult to distinguish visually, particularly at the early vegetative stage. Most publicly available leaf-image datasets used to train computer-vision models for crop diagnosis are captured under controlled laboratory conditions with plain backgrounds, which limits the field-readiness of models trained on them. FieldMaizeLeaf-8 addresses this gap by providing 3975 RGB photographs of maize leaves aged approximately 1–2 months, captured directly in commercial maize fields in Daudkandi, Cumilla, Bangladesh, using a consumer smartphone under natural daylight, background, and viewing-angle conditions, with no laboratory staging. Images are organised into eight classes: one healthy-leaf class and seven stress classes, comprising three biotic conditions (Fall Armyworm damage, Leaf Spot, and Northern Corn Leaf Blight) and four abiotic nutrient-deficiency conditions (Nitrogen, Phosphorus, Potassium, and Magnesium deficiency). Class labels assigned in the field were subsequently reviewed and validated by a plant-pathology expert. The dataset retains natural variability in illumination, leaf orientation, and symptom severity, and is intended to serve as a real-field benchmark for developing and evaluating deep-learning models for maize stress recognition, transfer learning, and explainable artificial intelligence (XAI) applications in precision agriculture.",
  "abstract_shown": true,
  "affiliations": [
    "Data Science Lab, Department of Software Engineering, Daffodil International University, Daffodil Smart City, Savar, Dhaka, 1216, Bangladesh",
    "Department of Plant Pathology, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, 1207, Bangladesh"
  ],
  "article_type": "data paper",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Laboni Akter"
    },
    {
      "affs": [
        0
      ],
      "name": "Md Shohel Arman"
    },
    {
      "affs": [
        0
      ],
      "name": "Sakibul Hasan Chowdhury"
    },
    {
      "affs": [
        1
      ],
      "name": "Sayed Mohammad Mohsin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7xd443rhcet6k.bib",
  "cited_by": 0,
  "doi": "10.1016/j.dib.2026.113337",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734969/",
  "id": "7xd443rhcet6k",
  "issns": [
    "2352-3409"
  ],
  "landing_url": "https://doi.org/10.1016/j.dib.2026.113337",
  "language": "en",
  "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",
      "Data Article"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7204692460",
  "pdf_url": null,
  "pmcid": "PMC13734969",
  "pmid": "42990968",
  "published": "2026-10-09",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/7xd443rhcet6k.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:45Z",
      "native_id": "PMC13734969",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.dib.2026.113337"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "FieldMaizeLeaf-8: A real-field early vegetative maize leaf stress image dataset from Bangladesh",
  "title_text": "FieldMaizeLeaf-8: A real-field early vegetative maize leaf stress image dataset from Bangladesh",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:20:45Z",
  "url": "https://research.draconitic.com/paper/7xd443rhcet6k",
  "venue": "Data in Brief",
  "versions": []
}