Ensuring the supply of high-quality sweet potatoes to consumers requires efficient sorting of harvested produce, a task made complex by various factors. This paper introduces a prototype of an innovative automated sweet potato sorting system and its preliminary evaluation. The system integrates a machine vision-based grading module and a pneumatic actuation cylinder-based sorting mechanism. The vision system captures multiple views of rotating sweet potatoes on a conveyor, utilizing a deep learning algorithm to track and grade them based on size, shape, and surface defects. The integrated sorting mechanism, activated by a computer-controlled cylinder, automatically segregates the sweet potatoes into designated areas based on quality grades. Future experimentation aims to quantify the efficacy of the integrated system, promising a potentially valuable tool for sweet potato packers.
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