DEVELOPMENT AND EXPERIMENTAL EVALUATION OF A REAL-TIME EGG QUALITY DETECTION SYSTEM BASED ON DIGITAL IMAGE PROCESSING AND YOLO MODEL ON EDGE DEVICES
Abstract
The demand for chicken eggs as a nutritious protein source continues to rise, yet automated inspection technology in small-scale farms remains limited due to high costs. This study develops a standalone, low-cost real-time egg quality detection prototype based on the edge computing paradigm. The system is implemented on a Raspberry Pi 4 Model B using the YOLOv8n deep learning model to classify eggs into three categories: Good, Cracked, and Broken. Visual data is acquired via a Raspberry Pi Camera Module 3 supported by controlled white LED ring lighting at a fixed distance of 20 cm to mitigate environmental light variations. Experimental evaluation using 1,080 samples indicates that the system achieves an optimal accuracy of 91.30% at 320x320 resolution under controlled lighting. Technically, the system demonstrates stable performance with CPU usage ranging from 43% to 76%, while maintaining temperatures at 48-510C. Despite a processing speed of 0.5-0.6 FPS, the system's independence from cloud connectivity makes it a highly applicable objective inspection solution for small-scale farmers in regions with limited digital infrastructure.
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