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

doi:10.63158/journalisi.v8i5.1882

Authors

Sriyadi Nusamandiri University
Jufriadif Na'am Nusamandiri University
Mochamad Wahyudi Universitas Bina Sarana Informatika
Sumanto Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.63158/journalisi.v8i5.1882

Keywords:

egg fertility classification, egg candling, computer vision, image classification, machine learning, agricultural automation, image processing

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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References

[1] X. Yang, R. B. Bist, S. Subedi, and L. Chai, “A Computer Vision-Based Automatic System for Egg Grading and Defect Detection,” Animals, vol. 13, no. 14, 2023, doi: 10.3390/ani13142354.

[2] R. L. M. Bambilla, M. J. B. Lee, and F. L. Valiente, “Freshness Condition Detection, Size Classification with Weight Prediction and Sortation of Philippine Egg Produce Using YOLOv8 and OpenCV,” in 2025 17th International Conference on Computer and Automation Engineering, ICCAE 2025, School of Electrical, Electronics and Computer Engineering, Mapúa University, Manila, Philippines: Institute of Electrical and Electronics Engineers Inc., 2025, pp. 158–164. doi: 10.1109/ICCAE64891.2025.10980499.

[3] H. A. Muslim, H. Oktavianto, R. T. Widodo, E. Purwantini, and W. T. Sesulihatien, “Implementation of Chicken Eggs Fertility Detection Device Using SSD MobileNet-V2 FPNLite,” 2024 Int. Electron. Symp. Shap. Futur. Soc. 5.0 Beyond, IES 2024 - Proceeding, pp. 171–177, 2024, doi: 10.1109/IES63037.2024.10665784.

[4] D. Sun, H. Wei, Y. Luo, W. Li, and Y. Yu, “Real-Time Detection Method for Cracked Eggs Based on Improved YOLOv8 and Dual Verification with Triple Classification Granularity,” IEEE Access, 2026, doi: 10.1109/ACCESS.2026.3686452.

[5] S. L. de Castro Júnior, A. E. C. M. Cândido, A. C. de S. Silva, and I. J. O. da Silva, “Translucency of chicken eggs: proposal of an automated system for analysis and classification,” Poult. Sci., vol. 104, no. 10, 2025, doi: 10.1016/j.psj.2025.105612.

[6] T. D. Pham, “Classification of Motor-Imagery Tasks Using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 1097–1107, 2023, doi: 10.1109/TNSRE.2023.3241241.

[7] P. Zeng et al., “High-Accuracy Classification of Fertilized Eggs Using Mamba-Based Model,” in 2025 6th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2025, School of Physics and Information Engineering, Guangdong University of Education, Guangzhou, 510303, China: Institute of Electrical and Electronics Engineers Inc., 2025, pp. 92–96. doi: 10.1109/AIEA66061.2025.11159668.

[8] P. G. Gülhan and G. Özmen, “Follicle Detection for Polycystic Ovary Syndrome by Using Image Processing Methods,” Int. J. Appl. Math. Electron. Comput., vol. 8, no. 4, pp. 203–208, 2020, doi: 10.18100/ijamec.803400.

[9] C. Shi et al., “Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model,” Agric., vol. 12, no. 8, p. 1137, 2022, doi: 10.3390/agriculture12081137.

[10] S. Anantharajan and S. Gunasekaran, “Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques,” Int. J. Imaging Syst. Technol., vol. 31, no. 4, pp. 1970–1988, 2021, doi: 10.1002/ima.22582.

[11] I. K. G. D. Putra, N. P. A. O. Wiastini, K. S. Wibawa, and I. M. S. Putra, “Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine,” Int. J. Mach. Learn. Comput., vol. 10, no. 4, pp. 542–548, 2020, doi: 10.18178/ijmlc.2020.10.4.970.

[12] D. M. Toufiq, A. M. Sagheer, and H. Veisi, “Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis,” Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2588–2597, 2021, doi: 10.11591/eei.v10i5.3013.

[13] F. A. Jibon et al., “Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation,” Healthc., vol. 10, no. 9, p. 1801, 2022, doi: 10.3390/healthcare10091801.

[14] S. K. Prabhakar and D. O. Won, “Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals,” Front. Artif. Intell., vol. 6, 2023, doi: 10.3389/frai.2023.1156269.

[15] D. Saha and A. Manickavasagan, “Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review,” Curr. Res. Food Sci., vol. 4, pp. 28–44, 2021, doi: 10.1016/j.crfs.2021.01.002.

[16] G. Elibol, M. Yumurtaci, S. Ergi̇n, and İ. Yabanova, “Classification of Dynamic Egg Weights Using Feature Extraction Methods,” Eskişehir Tech. Univ. J. Sci. Technol. a - Appl. Sci. Eng., vol. 21, no. 4, pp. 499–513, 2020, doi: 10.18038/estubtda.658077.

[17] A. O. Adegbenjo, L. Liu, and M. O. Ngadi, “Non-destructive assessment of chicken egg fertility,” Sensors (Switzerland), vol. 20, no. 19, pp. 1–23, 2020, doi: 10.3390/s20195546.

[18] D. Kalpana and K. S. Reddy, “SVM for Classification of Brain MRI with K-Means for Detection,” Interantional J. Sci. Res. Eng. Manag., vol. 07, no. 11, pp. 1–11, 2023, doi: 10.55041/ijsrem26641.

[19] G. Yuan, Y. Liu, W. Huang, and B. Hu, “Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI,” Comput. Math. Methods Med., vol. 2020, pp. 1–14, 2020, doi: 10.1155/2020/6913418.

[20] A. Nanda, R. C. Barik, and S. Bakshi, “SSO-RBNN driven brain tumor classification with Saliency-K-means segmentation technique,” Biomed. Signal Process. Control, vol. 81, 2023, doi: 10.1016/j.bspc.2022.104356.

[21] S. Fooladi, H. Farsi, and S. Mohamadzadeh, “Segmenting the Lesion Area of Brain Tumor using Convolutional Neural Networks and Fuzzy K-Means Clustering,” Int. J. Eng. Trans. B Appl., vol. 36, no. 8, pp. 1556–1568, 2023, doi: 10.5829/IJE.2023.36.08B.15.

[22] O. S. Faragallah, H. M. El-Hoseny, and H. S. El-sayed, “Efficient brain tumor segmentation using OTSU and K-means clustering in homomorphic transform,” Biomed. Signal Process. Control, vol. 84, 2023, doi: 10.1016/j.bspc.2023.104712.

[23] A. Saday and İ. A. Ozkan, “Classification of Epileptic EEG Signals Using DWT-Based Feature Extraction and Machine Learning Methods,” Int. J. Appl. Math. Electron. Comput., vol. 9, no. 4, pp. 122–129, 2021, doi: 10.18100/ijamec.988691.

[24] I. Zine-Dine, J. Riffi, K. El Fazazy, I. El Batteoui, M. A. Mahraz, and H. Tairi, “A review: Machine Learning Techniques of Brain Tumor Classification and Segmentation,” Mach. Graph. Vis., vol. 34, no. 3, pp. 31–55, 2025, doi: 10.22630/MGV.2025.34.3.2.

[25] A. Tayeh Swadi and F. Sabar Miften, “Detection of attention deficit hyperactivity disorder based on EEG signals using Least Square Support Vector Machine (LS-SVM),” Comput. Methods Biomech. Biomed. Eng. Imaging Vis., vol. 11, no. 6, pp. 2495–2507, 2023, doi: 10.1080/21681163.2023.2243347.

[26] K. P. Ajitha Gladis, A. Ahilan, N. Muthukumaran, and L. Jenifer, “ECG Arrhythmia Measurement and Classification for Portable Monitoring,” Meas. Sci. Rev., vol. 24, no. 4, pp. 118–128, 2024, doi: 10.2478/msr-2024-0017.

[27] P. Yan, Y. Zhang, S. Hou, W. Zhang, and X. Yang, “Noise reduction method for blasting vibration signals based on improved EMD-wavelet packet,” Zhendong yu Chongji/Journal Vib. Shock, vol. 43, no. 11, pp. 264-271and287, 2024, doi: 10.13465/j.cnki.jvs.2024.11.029.

[28] H. Yang, Y. Zhai, J. Liang, D. Guo, X. Liu, and R. Zhang, “Algorithm for Extracting Wear Characteristics of Piston Pins Based on Orthogonal Empirical Mode Decomposition,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 55, pp. 412–419, 2024, doi: 10.6041/j.issn.1000-1298.2024.S1.044.

[29] K. K. Çevik, H. E. Koçer, and M. Boğa, “Deep Learning Based Egg Fertility Detection,” Vet. Sci., vol. 9, no. 10, 2022, doi: 10.3390/vetsci9100574.

[30] J. L. Dioses, R. P. Medina, A. C. Fajardo, and A. A. Hernandez, “Performance of Classification Models in Japanese Quail Egg Sexing,” in Proceeding - 2021 IEEE 17th International Colloquium on Signal Processing and Its Applications, CSPA 2021, Graduate School, Technological Institute of the Philippines, Quezon City, Philippines: Institute of Electrical and Electronics Engineers Inc., 2021, pp. 29–34. doi: 10.1109/CSPA52141.2021.9377275.

[31] Y. F. Wei, P. Yang, Z. Y. Yang, P. Wang, and X. W. Wang, “Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN,” J. Electr. Eng. Technol., vol. 20, no. 1, pp. 217–229, 2025, doi: 10.1007/s42835-024-02038-9.

[32] M. H. Saleem, J. Potgieter, and K. M. Arif, “Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments,” Precision Agriculture, vol. 22, no. 6. pp. 2053–2091, 2021. doi: 10.1007/s11119-021-09806-x.

[33] S. Saifullah, R. Drezewski, A. Khaliduzzaman, L. K. Tolentino, and R. Ilyos, “K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 8, no. 2, p. 175, 2022, doi: 10.26555/jiteki.v8i2.23724.

[34] V. Kiruthika and M. M. Ramya, “Automatic segmentation of ovarian follicle using K-means clustering,” Proc. - 2014 5th Int. Conf. Signal Image Process. ICSIP 2014, pp. 137–141, 2014, doi: 10.1109/ICSIP.2014.27.