The removal of pineapple eyes is a crucial step in pineapple processing. However, their irregularly distributed spiral arrangement presents a dual challenge for positioning accuracy and automated removal by the end-effector. In order to solve this problem, a pineapple eye removal device based on machine vision was designed. The device comprises a clamping mechanism, an eye removal end-effector, an XZ two-axis sliding table, a depth camera, and a control system. Taking the eye removal time and rotational angular velocity as variables, the relationship between the rod length of the prime mover and the contact force and gear torque during the eye removal process was simulated and analyzed using ADAMS (2020) software. Based on these simulations, the optimal length of the prime mover for the end-effector was determined to be 23.00 mm. The performance of various YOLOv5 models was compared in terms of accuracy, recall rate, mean detection error, and detection time. The YOLOv5s model was chosen for real-time pineapple eye detection, and the eye’s position was determined through coordinate transformation. The control system then actuated the XZ two-axis sliding table to position the eye removal end-effector for effective removal. The results indicated an average complete removal rate of 88.5%, an incomplete removal rate of 6.6%, a missed detection rate of 4.9%, and an average removal time of 156.7 s per pineapple. Compared with existing solutions, this study optimized the end-effector design for pineapple eye removal. Depth information was captured with a depth camera, and machine vision was combined with three-dimensional localization. These steps improved removal accuracy and increased production efficiency.
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