{
  "abstract": "Egg fertility assessment is essential for improving hatchery efficiency, reducing incubation costs, and minimizing resource waste caused by infertile eggs. This study aims to develop a computer vision-based framework for three-level egg fertility classification, namely Highly Fertile, Fertile, and Infertile. A balanced dataset consisting of 1,500 manually collected candling images (500 images per class) was validated by three poultry experts. The proposed method involves L*a*b* color-space transformation, K-Means clustering for region-of-interest segmentation, Discrete Wavelet Transform (DWT) using the LL sub-band for texture feature extraction, and multiclass Support Vector Machine (SVM) classification. The experimental results show that the proposed model achieves 90.9% training accuracy, with precision, recall, and F1-score of 92.0%, 90.9%, and 90.5%, respectively. For the independent testing dataset, the model achieves 89.7% accuracy and 89.4% F1-score, indicating good generalization capability with only a 1.2% difference between training and testing accuracy. The novelty of this research is the development of a three-level egg fertility classification framework that extends conventional binary fertility detection by integrating L*a*b*-based segmentation, LL-wavelet statistical texture features, and multiclass SVM classification.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Sriyadi"
    },
    {
      "name": "Jufriadif Na'am"
    },
    {
      "name": "Mochamad Wahyudi"
    },
    {
      "name": "Sumanto"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b65687acheafd.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1882",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1882",
  "id": "b65687acheafd",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1882",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7223281878",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1882/1088",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/b65687acheafd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:46Z",
      "native_id": "83a10e1c32674c7b94e107a3d96204a1",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1882"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "A Computer Vision-Based Framework for Multilevel Egg Fertility Classification Using L*a*b* Color Segmentation, Wavelet Texture Features, and Support Vector Machine",
  "title_text": "A Computer Vision-Based Framework for Multilevel Egg Fertility Classification Using L*a*b* Color Segmentation, Wavelet Texture Features, and Support Vector Machine",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:46Z",
  "url": "https://research.draconitic.com/paper/b65687acheafd",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}