<![CDATA[Abstract: Industrial sorting on small-scale production lines is often still performed manually, which can reduce throughput consistency, increase human error, and limit real-time traceability. Objective: design, implement, and evaluate an ESP32-based conveyor prototype that automatically sorts items by height while providing local and IoT-based monitoring. Methodology: engineering experimental approach (prototype development and verification testing). Data were collected from embedded sensor readings (HC-SR04 ultrasonic and IR sensors), actuator response observations, LCD outputs, IoT dashboard records, and manual reference measurements for validation. Data were analyzed by assessing measurement accuracy, detection reliability, actuator responsiveness, and overall sorting success rate. Findings: HC-SR04 estimated item height with good accuracy (average deviation approximately ±0.1 cm), supported by interrupt-based handling and data averaging to improve stability. IR sensors reliably detected item presence and position, while pull-up configuration and debounce logic prevented duplicate triggering. MG90S 180° servo actuator performed smooth category-based diversion using non-blocking and soft-open control without disrupting other system processes. Sorting success rate exceeded 95%, and operation remained stable in both offline and online modes, with IoT integration enabling real-time monitoring without becoming a dependency for control. Implications: proposed architecture can function as a low-cost learning platform and a basis for small-scale industrial automation requiring reliable sorting and operational visibility. Originality/value: integration of baseline-calibrated ultrasonic height measurement, IR-based position gating, non-blocking servo control, local 20×4 I2C LCD feedback, and IoT monitoring into a single workflow that remains functional during network disruptions.]]>
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