Development and Performance Evaluation of a Vision-Based Automated Oyster Size Classification System

This study presents the development and validation of an automated oyster classification system designed to classify oysters by size and place them into trays for freezing. Addressing limitations in conventional manual processing, the proposed system integrates a vision-based recognition algorithm and a delta robot (parallel robot) equipped with a soft gripper. The vision system identifies oyster size and optimal grasp points using image moment calculations, enhancing the accuracy of classification for irregularly shaped oysters. Experimental tests demonstrated classification and grasping success rates of 99%. A process simulation based on real industrial conditions revealed that seven units of the automated system are required to match the daily output of 7 tons achieved by 60 workers. When compared with a theoretical 100% success rate, the system showed a marginal production loss of 715 oysters and 15 trays. These results confirm the potential of the proposed system to improve consistency, reduce labor dependency, and increase productivity in oyster processing. Future work will focus on gripper design optimization and parameter tuning to further improve system stability and efficiency.