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Feri Candra
Universitas Riau
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Evaluasi Kinerja YOLOv8 dalam Klasifikasi Kualitas Telur Berbasis Warna dan Tekstur Cangkang Khairy, Khafizh; Candra, Feri
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.326-339

Abstract

Egg quality plays a vital role in the food industry, directly affecting shelf life, food safety, and consumer health. Conventional quality assessment methods, such as manual inspection and laboratory testing, are often time-consuming, labor-intensive, and prone to subjectivity, leading to inconsistent classification results. To address these challenges, this research proposes the development of an automated egg quality classification system based on computer vision and artificial intelligence. The system focuses on evaluating external egg characteristics—specifically shell color and texture—using a combination of Convolutional Neural Network (CNN) for feature extraction and the YOLO (You Only Look Once) algorithm for real-time object detection and classification. The development stages include dataset collection, image preprocessing (such as augmentation and segmentation), model training, and performance evaluation using accuracy, precision, recall, and F1-score. The goal is to achieve an accuracy rate above 90% in classifying eggs into quality categories. This study evaluates the performance of YOLOv8 for automatic egg quality classification based on shell color and texture. A dataset consisting of 1,200 egg images was collected from both production facilities and online sources, and labeled into three categories: Good, Fair, and Poor quality. The model was trained on Google Colab with GPU acceleration using a batch size of 16, learning rate of 0.001, and 50 epochs. Performance was assessed using mean Average Precision (mAP), precision, and recall, where the results achieved mAP of 0.87, average precision of 0.91, and recall of 0.89. The “Fair” class obtained lower accuracy (72%) due to high visual similarity with the “Good” class and dataset imbalance (250 images vs. 450 images for “Good”). Compared to previous studies that reported mAP ≈ 0.80 using YOLOv5, this research demonstrates improved performance and highlights YOLOv8 as a more competitive solution for industrial egg quality control. This work contributes a practical implementation pipeline and an analysis of visual factors influencing misclassification. Future developments include dataset expansion, advanced balancing techniques, and real-time industrial deployment testing.
Co-Authors Adiya, M. Hasmil Afdi Rizal Ahmad Syahfrizal Akbar, Nurhadi Aminuyati Anand, Barri Andhi, Rahmat Rizal Angriawan, Sherkhing Anhar Anhar Antonius Rajagukguk Bayu Fharadila Chairun Nisa Chi-chi Salsa Amaza Dani, Febry Rachma Darwin Manurung Delsavonita, Delsavonita Desnelita, Yenny Dewi Nasien Dina Lovita Sari Dina Veranita Edi Susilo Edi Susilo Efendi, Mas Esa Prakasa Esa Prakasa, Esa Faisal Karim Fathra Annis Nauli Fathur Rohman, Fathur Febiola, Dwi Suci Giovan Alshary Gressiva, Gressiva Hadiwandra, T Yudi Hamdani, Eddy Hana Bernika Sabila Herman Herman Hidayat, Ilham I. Yasri Indra Yasri IRSAN TAUFIK ALI JR Lessy Eka Putri Khairy, Khafizh Kurniawan Kurniawan Lukmannil Hakim Luthfi Afif Mailestari Wina Yance Minarni Minarni Minarni Shiddiq, Minarni Misrawati Misrawati Mohammad Fisal Rabin Monica Oktavianti Muhamad Zahara Anugrah Putra Muhammad Rizki Radhelan Muhammad Sandy Prastyo Muhammad Syafii Muhammad Syafii Nadira Alifia Ionendri Naufal Fikri Aulia Ningsih, Sri Purnama Noprizal Noprizal Noprizal, Noprizal Noveri Lysbetti M Novtarina, Dita Aulia Nurhalim Dani Ali Putra, Muhamad Zahara Anugrah Rabil Kurniawan RAHMAT WIBOWO Ramadhan Ramadhan Ramadhan Ramadhani, Dian Rani, Mutia Mustika Refli Erdinal Restuyoda, Raka Rezkiana, Nisa Tri Roby Esta Sunara Roni Sanjaya Saktioto Saktioto Salhazan Nasution Salsabilla Azahra Putri Setiabudi, Muhamad Indra Siti Komariah Siti Komariah Soesilo, Eddy Sri Wahyuni Suryani Suryani Syafiqoh, Nidya Nur Warman Fatra, Warman Yanda Sepri Yanifal Yudi Hadiwandra, Tengku Yusnita Rahayu Zulharman Zulharman Zulharman Zulharman