<![CDATA[The classification of layer chicken eggs (Gallus gallus domesticus) in small- and medium-scale farms is still commonly carried out manually through weighing and visual inspection of shell conditions. This process may produce different results between operators and requires considerable time for sorting. This study aimed to design and develop an automatic classification system for layer chicken eggs based on a conveyor using an ESP32 microcontroller. The system classifies eggs based on weight, shell crack condition, and shell color, while displaying the classification results through an Internet of Things (IoT)-based monitoring dashboard. The research employed the ADDIE development method, consisting of analysis, design, development, implementation, and evaluation stages. The system uses a load cell HX711 sensor to measure egg weight, an LDR sensor to detect shell cracks, and a TCS3200 color sensor to identify shell color. Testing was conducted on 30 egg samples with a weight range of 20–80 grams. The results showed that the load cell sensor produced an average measurement error of 3.44%, the LDR sensor detected egg conditions with a success rate of 100%, and all 30 samples were processed without data loss, with an average processing time of 14.56 seconds per egg. The monitoring dashboard was also able to display classification results in real time according to the actual egg conditions on the conveyor. These results indicate that the developed system integrates weight, shell crack condition, and shell color classification into an ESP32-based automatic system and provides monitoring of the classification results through an IoT-based dashboard.]]>
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