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Peer-reviewedCC BYJournal of Information Systems and Informatics

A Computer Vision-Based Framework for Multilevel Egg Fertility Classification Using L*a*b* Color Segmentation, Wavelet Texture Features, and Support Vector Machine

  • Sriyadi
  • Jufriadif Na'am
  • Mochamad Wahyudi
  • Sumanto
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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.

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Cite

BibTeXRIS
BibTeX
@article{sriyadi2026computer,
  title = {{A Computer Vision-Based Framework for Multilevel Egg Fertility Classification Using L*a*b* Color Segmentation, Wavelet Texture Features, and Support Vector Machine}},
  author = {Sriyadi and Jufriadif Na'am and Mochamad Wahyudi and Sumanto},
  journal = {Journal of Information Systems and Informatics},
  year = {2026},
  month = oct,
  doi = {10.63158/journalisi.v8i5.1882},
  url = {https://doi.org/10.63158/journalisi.v8i5.1882},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A Computer Vision-Based Framework for Multilevel Egg Fertility Classification Using L*a*b* Color Segmentation, Wavelet Texture Features, and Support Vector Machine
AU  - Sriyadi
AU  - Jufriadif Na'am
AU  - Mochamad Wahyudi
AU  - Sumanto
PY  - 2026
DA  - 2026/10/24
JO  - Journal of Information Systems and Informatics
DO  - 10.63158/journalisi.v8i5.1882
UR  - https://doi.org/10.63158/journalisi.v8i5.1882
AB  - 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.
SN  - 2656-5935
SN  - 2656-4882
ER  -