Online Identification of Defect Bottles in Production Line of Soft Drink using Machine Vision

ماشین های کشاورزی

Article 10, Volume 9, Issue 1 - Serial Number 17, 2019, Pages 123-137   

Document Type: Research Article

DOI: 10.22067/jam.v9i1.64834

Authors

V. Rasooli Sharabiani^{* 1}; O. Farhangi¹; E. Taghinezhad²

¹Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

²Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

Introduction
Nowadays the packaging of a product is considered as a symbol of its quality and has a direct effect on its consumer-satisfaction and sales. The visual inspection method is much slower and more error-prone than that of automated method which is used for mass production. Also, this method has other problems such as high labor cost, fatigue, low accuracy, and inconsistency due to various environmental conditions such as lighting, or lack of concentration as well as lack of standards and skilled worker. Machine vision has different applications in the industry. In the packaging of liquid such as cooking oils and different beverages (mineral water, soft drinks, fruit juices ...) that liquid can leak out. So, inspecting cap defects, seal ring defects and liquid level are urgent. Also, label placement plays an important role in customer satisfaction. Machine vision can be able to detect these defects; therefor its application will be effective and useful. So, the advantages of machine vision are non-destructive, accurate, and consistent. Researchers have been used the machine vision system for different area including inspection of surface and structural flaw inspection; steel strips and pharmaceutical tablets. Also, machine vision was used for online control of grading and separation of different agricultural products, such as kiwi, pomegranate, dates, cucumber, almonds, potatoes, tomato and peach. The aim of this research was to manufacture and application a system based on machine vision for inspection and classification of defects in bottles on production lines (case study: soft drink). Sample quality was included of three defects: cap defaults, liquid level and label placement.
 Materials and Methods
300cc Coca Cola bottles were used as samples for this research. The research was performed to inspect the common defects, including of the cap defaults, liquid level and label placement. In this research, a bottle classification system was designed and developed which it consists of hardware and a software unit. The hardware includes of a conveyor belt, a power system and a power transmission unit, light source, a digital camera, a mechanical ejector and a computer. In this project Lab view 2011 software was used. In this online system, decision was done based on Boolean logic and the defected bottles were separated from the normal ones. For image acquisition and algorithm design the different steps were followed: Vision acquisition, image processing and programming. Clamp (Rake) function was used for inspection of liquid level. It calculated the maximum distance between the cap and liquid level and its result was compared to the edge strength and threshold level. Inspection of cap defaults and label placement was performed using pattern matching and edge detection algorithm, respectively. The appropriate time of ejector must be calculated to take defective samples out of the production line.
 Results and Discussion
Research results were reported at four parts including of cap defects, liquid level and label placement inspection furthermore the combination of all three groups. To find of inspection accuracy, it was repeated 100 times for each default. Accuracy of inspection of the cap, label placement and liquid level were earned as 95, 90 and 100%, respectively. The average accuracy of system was 95.6%. With regard to the conveyor belt’s speed (20cm s^-1) and the distance (10cm) between the bottles, the required time to inspect each bottle was 500ms. So, program’s performance was acceptable according to process time of 150-250ms. Finally, the operational capacity of the system was 7200 bottles per hour. These findings were similar to results reported by other researchers. They reported the accuracy of 97 and 90.61% for beer bottles inspection and tomatoes separating, respectively.
 Conclusion
Average of total accuracy for this system was obtained as 95.6%. It separately was 100, 95 and 92% for inspection of liquid level, cap, and label placement, respectively. The highest and lowest accuracy were for inspection of liquid level and label placement. So, performance of the algorithm was suitable for use on production lines. Also, it will be applicable to liquid packaging in the food industry, chemical industry and so on.

Keywords

Machine vision; Packaging; Production line; Quality control; Soft drink

References

Abdelhedi, S., K. Taouil, and B. Hadjkacem. 2012. Design of Automatic Vision-based Inspection System for Monitoring in an Olive Oil Bottling Line. International Journal of Computer Applications 51: 39-46.

2. AhmadKhani, S., A. Mahmoodi, K. Mollahzade, and H. Ghafari. 2014. Prediction of peach fruit firmness using imaging system of laser light scattering. Biosystem Engineering 46: 229-234. (In Farsi).

3. Al Ohali, Y. 2011. Computer vision-based date fruit grading system: Design and implementation. Journal of King Saud University - Computer and Information Sciences 23: 29-36.

4. Arjenaki, O. O., P. A. Moghaddam, and A. M. Motlagh. 2013. Online tomato sorting based on shape, maturity, size, and surface defects using machine vision. Turkish Journal of Agriculture and Forestry 37: 62-68.

5. Avendano, J., P. J. Ramos, and F. A. Prieto. 2017. A system for classifying vegetative structures on coffee branches based on videos recorded in the field by a mobile device. Expert Systems with Applications 88: 178-192.

6. Azizi, A., Y. Abbaspour-Gilandeh, M. Nooshyar, and A. Afkari-Sayah. 2016. Identifying Potato Varieties Using Machine Vision and Artificial Neural Networks. International Journal of Food Properties 19: 618-635.

7. Bennedsen, B. S., and D. L. Peterson. 2005. Performance of a System for Apple Surface Defect Identification in Near-infrared Images. Biosystems Engineering 90: 419-431.

8. Blasco, J., S. Cubero, J. Gomez-Sanchis, P. Mira, and E. Molto. 2009. Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering 90: 27-34.

9. Duan, F., Y.-N. Wang, H.-J. Liu, and Y.-G. Li. 2007. A machine vision inspector for beer bottle. Engineering Applications of Artificial Intelligence 20: 1013-1021.

10. ElMasry, G., S. Cubero, E. Molto, and J. Blasco. 2012. In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering 112: 60-68.

11. Fehr, B. W., and J. B. Gerrish. 1995. Vision-guided row crop follower. Applied Engineering in Agriculture 11: 613-620.

12. Guyer, D., and X. Yang. 2000. Use of genetic artificial neural networks and spectral imaging for defect detection on cherries. Computers and Electronics in Agriculture 29: 179-194.

13. Islam, M., R. Sahriar, and B. Hossain. 2012. An enhanced automatic surface and structural flaw inspection and categorization using image processing both for flat and textured ceramic tiles. International Journal of Computer Applications 48: 1-10.

14. Jin, J., J. Li, G. Liao, X. Yu, L. Christopher, and C. Viray. 2009. Methodology for Potatoes Defects Detection with Computer Vision. in International Symposium on Information Processing. Huangshan, P. R. China.

15. Jinshi, C., Y. Myongkyoon, S. Daesik, and C. Seong-In. 2017. Machine vision and thermographic imaging for determining of grading of tomato on postharvest. 2017 ASABE Annual International Meeting: 1.

16. Khan, S., T. Mulani, P. Lalge, and N. Shaikh. 2017. An Image Processing Technique for Grading of Harvested Mangoes. International Research Journal of Engineering and Technology 4: 825-828.

17. Kheiralipour, K., and A. Pormah. 2017. Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering: DOI: 10.1111/jfpe.12558.

18. KheyrAlipour, K., V. Mohammadi, and M. GhasemiVarnamkhasti. 2014. Persimmon fruit ripening detection using image processing and support vector machine. 9th National Congress of Agricultural Machinery Engineering (Mechanical Biosystems and mechanization). Tehran University. (In Farsi).

19. Kumar, D. P., and K. Kannan. 2010. A roadmap for designing an automated visual inspection system. International Journal of Computer Applications 1: 34-37.

20. Manickavasagan, A., N. K. Al-Mezeini, and H. N. Al-Shekaili. 2014. RGB color imaging technique for grading of dates. Scientia Horticulturae 175: 87-94.

21. Mozina, M., D. Tomaževič, F. Pernuš, and B. Likar. 2009. Real-time image segmentation for visual inspection of pharmaceutical tablets. Machine Vision and Applications 22: 145-156.

22. Nouri-Ahmadabadi, H., M. Omid, S. S. Mohtasebi, and M. Soltani Firouz. 2017. Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine. Information processing in agriculture: http://dx.doi.org/10.1016/j.inpa.2017.1006.1002.

23. Omidi, A. A., P. ModaresMotlagh, and M. Ahmadi. 2012. Design, construction and evaluation of intelligent grading system of tomato. 9th National Congress of Agricultural Machinery Engineering (Mechanical Biosystems and mechanization). Shiraz University. (In Farsi).

24. Tachwali, Y., Y. Al-Assaf, and A. R. Al-Ali. 2007. Automatic multistage classification system for plastic bottles recycling. Resources, Conservation and Recycling 52 (2): 266-285.

25. Teymori, N., M. Omid, K. MoharamZadeh, and A. Rajabipour. 2015. Almond separation of conjoined and qualitative classification of a combination of image processing techniques and networks ANN. Biosystem Engineering 46: 355-362. (In Farsi).

26. Younes, M. A., S. Darwish, and M. El-Sayed. 2011. Online quality monitoring of perforated steel strips using an automated visual inspection (AVI) system. in IEEE International Conference on Quality and Reliability (ICQR). Bangkok, Thailand

27. Yousefdad, M., A. M. AfkariSayah, M. R. Larijani, and Y. AbbaspourGilande. 2014. Compare kiwi healthy and defective fruits based on color components using image processing techniques. in Advances in engineering and basic science. Tehran. (In Farsi).

28. Zhang, Y., X. Yin, X. Zou, and J. Zhao. 2009. On-line sorting maturity of cherry tomato by machine vision. International Federation for Information Processing 295: 2223-2230.