Deteksi Dan Klasifikasi Kondisi Telur Ayam Ras Berdasarkan Kerusakan Kerabang Menggunakan YOLO
DOI:
https://doi.org/10.31598/sintechjournal.v8i3.2001Keywords:
egg detection, shell damage, YOLOv8, YOLOv11Abstract
Chicken farming is an important sector that provide animal protein sources, especially through egg production. The high demand for chicken eggs in Indonesia makes a fast and accurate sorting process to ensure quality very important. Until now, the sorting process has been done manually by human workers, which is time-consuming, costly, and prone tohuman error. This study aims to detect and classify the condition of chicken eggs based on shell damage using object detection algorithms such as YOLOv8 and YOLOv11. This experiment uses a dataset of 1280 images with four egg condition, intact, cracked or punctured, white, and soft. The dataset went through pre-processing, augmentation, training, validation, and testing stages. The results showed that both models were able to accurately detect and classify eggs, with YOLOv8 achieving a precision 0.997, a recall 1, an IoU 0.9345, mAP0.5 0.995, and mAP0.5-0.95 0.913, while YOLOv11 was slightly superior due to architectural improvements through the C3k2 and C2PSA blocks, achieving a precision 0.999, a recall 1, an IoU 0.9347, mAP0.5 0.995, and mAP0.5-0.95 0.912. Further research, is recommended using datasets with more complex backgrounds, varied lighting, and more diverse damage variations
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