Analysis of The Shape of Pressed Tarts from Image Processing

AHMAD ZAKI SHUKOR

Abstract


Monitoring quality of food processed in manufacturing industries is gaining its importance to ensure marketability in local and international markets. Food processed from manufacturing industries is usually graded in terms of quality; a shape defect would be regarded as a non-quality product and may be rejected or given away. In a semi or fully automated food manufacturing system, one of the most commonly used techniques is computer vision. This can be implemented by using a camera with a certain resolution and image processing to display results obtained. This project applies image processing technique to differentiate good and defective tart, according to its shape. The mold platform moves along with the conveyor by first reaching a pressing location (pneumatic cylinder) and later arriving under the view of the camera. A Raspberry Pi was used to connect the conveyor motor, pneumatic cylinder and the ultrasonic sensors. From the image acquired by the USB camera, the images are processed by edge detection and the number of circles identified for the tart and centroid position has been analyzed. The results show that a quality tart shape has number of circles less than 10, for the distance of the camera of 12, 15 and 18cm, with the 15cm distance from the camera gives a more accurate reading.

Keywords


Tart classification, food manufacturing, tart pressing, edge detection, segmentation

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References


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