## 4 Discussion  
This study proposes an integrated vision and robotic control system for the automatic sorting and gripping of oysters on a conveyor belt, aimed at enhancing sorting efficiency and reducing contamination risks in the aquatic food production and processing industry. The contributions of this work can be evaluated from both technical and practical perspectives. Using CNNs for oyster image processing proved highly effective, highlighting their essential role in modern computer vision systems. By combining CNN with robotics, the intelligence of the robotic system has been significantly improved, setting the stage for future advances in automated sorting and gripping. For real-time object detection and grasping angle prediction, YOLOv8-OBB was employed due to its efficiency and practicality. Unlike two-stage detectors like Oriented R-CNN--which first generate region proposals and then refine oriented boxes--YOLOv8-OBB is based on a single-stage architecture that directly regresses oriented bounding boxes. As a result, YOLOv8-OBB offers faster inference speeds, making it more suitable for real-time applications. Additionally, its support for ONNX format conversion enables deployment on embedded systems like the Raspberry Pi, further enhancing its versatility. In the experiments, YOLOv8-OBB showed robust performance in various scenarios. However, when oysters were stacked closely together, detection instability led to inaccurate position and angle estimations, affecting grasping accuracy. Expanding the training dataset and adjusting the network structure could improve detection performance under such conditions. Regarding the gripper, our four-fingered flexible claw is made of compliant material and is intentionally designed larger than an oyster, allowing it to compensate for minor jitter errors during grasping. This design ensures excellent robustness, enabling the gripper to maintain a stable grip even when faced with small deviations or unexpected disturbances. From a practical standpoint, the automated sorting system offers clear advantages over traditional manual methods. By reducing direct contact with oysters, it minimizes contamination risk--critical for food safety. Moreover, automation enhances productivity, reduces labor costs, and boosts operational efficiency, significantly improving the overall performance of the seafood processing industry. While the system worked well in both static and dynamic grasping scenarios, challenges remained, particularly in multi-object situations. In static grasping, stacked oysters caused detection instability, resulting in failed grasps. In dynamic grasping, rapid motion and high conveyor speeds introduced errors due to misalignment between the gripper and the oyster, further complicated by delays in gripper closure. Despite applying the KF and Low-Pass filters, oscillations in prediction curves persisted at higher speeds. To improve the success rate of oyster capture, this study proposes the following measures: (a) a more stable mounting method is used to stabilize the robot and camera, minimizing vibration and improving detection and gripping accuracy; (b) increasing the mounting height of the camera expands its field of view, enabling earlier detection and providing more reaction time for the robotic arm; (c) placing the camera further away from the robot allows the oyster to be recognized earlier, giving the robotic arm more time to make adjustments for a successful grab; (d) firmly securing the robot base and camera mount to reduce instability and vibration is crucial for dynamic grasping accuracy. This study provides a strong foundation for developing automated sorting and gripping systems in the food industry. Future research will focus on refining object detection models, particularly in multi-object and occlusion-prone environments. Additionally, optimizing system calibration and hardware setup will be key to improving performance in dynamic conditions.