<![CDATA[Tomato sorting is an important post-harvest handling process, but manual sorting may produce inconsistent classification and depends on operator judgment. This study designs and implements an Internet of Things (IoT)-based prototype for automated tomato sorting according to ripeness and size using an ESP32 microcontroller. The system integrates a TCS34725 color sensor for ripeness classification, an infrared proximity sensor for object detection, servo motors for sorting, a DC motor-driven roller grader for size separation, and the Blynk platform for remote monitoring and conveyor-speed control. The prototype was developed using a Research and Development approach and evaluated through functional testing of its main subsystems. The proximity sensor achieved a 90% detection success rate, the TCS34725 achieved 70% ripeness classification accuracy, the servo sorter and stopper achieved 100% synchronization in the tested conditions, and Blynk monitoring achieved an 80% success rate with a response latency below 200 ms under stable Wi-Fi conditions. The arithmetic mean of the four subsystem rates was 85%, but this value represents a descriptive summary rather than the end-to-end accuracy of the complete sorting system. The results demonstrate the functional feasibility of the prototype, while improvements in sorting efficiency and post-harvest loss reduction were not experimentally verified.]]>
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