Study of Machine Vision System for Automated Pineapple's Eyes Detection
Jiramate Yasoongnern, Puree Imhun, Chettapong Janya-Anurak, Alexander Brezing, Sirichai Torsakul, Anucha Watanapa · 2023
This study aimed to improve the production process of canned pineapple by introducing an algorithm that utilizes machine vision and image processing techniques to detect and locate pineapple eyes accurately after the peeling process. The result can be subsequently transmitted the coordinates to the automatic pineapple eyes removal machine. The algorithm's primary objective is to improve productivity, especially by reducing waste due to human error. Therefore, the prototype has been made. Including the image acquisition process and the rotating system to capture pineapple surface features, the system was operated by a stepper motor along with an inspection camera for data collection. The algorithm was created based on thresholding method, with the calibration, pineapple eyes can be located in term of position(x) and angle (φ) of rotation axis. In summary, from our design and experiment. Our algorithm results in a maximum error of position (x) at 4.871 mm and a maximum error of angle (φ) at 3.415 degree at the processing time of 0.68 millisecond, with the entire process taking 20.92 seconds.