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In the poultry industry, chicken egg quality is a crucial factor influencing the price and market appeal of the product. Manual assessment of egg quality based on shell color requires significant time and labor and is prone to human error. Therefore, the implementation of automation technology through artificial intelligence (AI) is necessary to enhance the efficiency and accuracy of this process. The YOLO (You Only Look Once) algorithm is a fast and accurate object detection method that can be applied to classify chicken eggs based on shell color. This research aims to develop an automatic detection system using YOLO to identify and categorize the quality of chicken eggs based on shell color. Images of chicken eggs were collected and annotated to train the YOLO model. After training, the model was tested on a new dataset to evaluate its detection and classification performance. The results of the study indicate that the YOLO algorithm can detect and classify chicken eggs with high accuracy, reducing the need for manual labor and speeding up the quality assessment process. The implementation of this system is expected to improve operational efficiency in the poultry industry, ensure consistent product quality, and provide an innovative solution to the challenges in chicken egg quality assessment.
Ningsih et al. (2024) studied this question.