Analysis of The Shape of Pressed Tarts from Image Processing

Authors

  • AHMAD ZAKI SHUKOR Universiti Teknikal Malaysia Melaka

Keywords:

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

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.

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Author Biography

AHMAD ZAKI SHUKOR, Universiti Teknikal Malaysia Melaka

FKE UTeM

CeRIA

References

Steve D., Ph.D. (n.d.). Robotics and Automation for the Food Industry.[Online],at:https://www.foodsafetymagazine.com/magazine-archive1/augustseptember-2014/ robotics-and-automation-for-the-food-industry/

Chris C. “How Robots are changing the food Industry and Improving Fulfillment.”[Online], at https://www.foodlogistics.com/3pl-4pl/article/12002261/ho w-robots-are-changing-the-food-industry-and-improving-fulfillment.

D. Trientin, B. Hidayat, S. Darana, Beef freshness classification by using color analysis, multi-wavelet transformation, and artificial neural network, International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), pp. 181-185, Bandung, Indonesia, October 2015.

S.R. Rupanagudi, B.S. Ranjani, P. Nagaraj, V.G. Bhat, A cost effective tomato maturity grading system using image processing for farmers, 2014 International Conference on Contemporary Computing and Informatics (IC3I), pp. 7-12, Mysore, India, November, 2014.

B. Mahale, & S. Korde, Rice quality analysis using image processing techniques, International Conference for Convergence for Technology, Pune, India, April 2014

M. M. R Dahapute,. K-mean Clustering for Segmentation of Irregular Shape Fruit Images under Various Illumination. International Conference on Modern Trends in Engineering Science and Technology (ICMTEST 2016), Vol. 2 (Issue 5), pp 1-5, Shahapur, Maharashtra, India, May 2016.

C. Sekhar, B. Tudu, and C. Koley. “A Machine Vision Technique for Grading of Harvested Mangoes Based on Maturity and Quality.” IEEE Sensors Journal, Vol. 16 (Issue 16): 6387-6396, August, 2016

L. B. Gama e, C. W. de Silva, and R. G. Gosine. Statistical Pattern Recognition for Cutter Positioning in Automated Fish Processing. Department of Mechanical Engineering University of British Columbia Vancouver, Canada.1993 IEEE Pac Rim

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Published

2018-10-26

How to Cite

SHUKOR, A. Z. (2018). Analysis of The Shape of Pressed Tarts from Image Processing. International Journal of Electrical Engineering and Applied Sciences (IJEEAS), 1(2), 27–32. Retrieved from https://ijeeas.utem.edu.my/ijeeas/article/view/4456

Issue

Vol. 1 No. 2 (2018): Electrical Engineering and Applied Sciences

Section

Articles

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