Utilizing Real-Time Image Processing for Monitoring Bacterial Cellulose Formation During Fermentation

https://doi.org/10.22146/agritech.49155

Darmawan Ari Nugroho(1*), Lilik Sutiarso(2), Endang Sutriswati Rahayu(3), Rudiati Evi Masithoh(4)

(1) Department of Agroindustrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(2) Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(3) Department of Food and Agricultural Products Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(4) Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(*) Corresponding Author

Abstract


In general, nata is a bacterial cellulose results from bacterial fermentation of Gluconacetobacter xylinus. During the fermentation process, bacterial cellulose accumulates on the surface of the medium and is eventually visible. The parameter of the end of the fermentation process is indicated by the formation of bacterial cellulose sheets with a certain thickness. During this time, the determination of the success of the fermentation process is done by direct observation of the thickness formed. In this way, the failure of the fermentation process cannot be detected early. Real-time monitoring during the fermentation period will be very helpful to monitor the speed of bacterial cellulose formation and early failure detection of the fermentation process. Currently, image processing has been widely used for various purposes. This study describes how to utilize image processing to monitor bacterial cellulose formation during the fermentation process. For this reason, it is necessary to modify the fermentor by making an area to shoot and follow any thickness increase in the bacterial cellulose, as well as painting the fermentor in dark color to better contrast with the bacterial cellulose color. The device used is the Raspberry Pi, which has been connected to a web camera. Once the image has been captured, it is then processed to calculate the thickness, after which the thickness data are sent to the database.

Keywords


Bacterial cellulose; image processing; Raspberry Pi

Full Text:

PDF


References

Álvarez-Bermejo, J. A., Giagnocavo, C., Li, M., Morales, E. C., Santos, D. P. M., & Yang, X. T. (2017). Image processing methods to evaluate tomato and zucchini damage in post-harvest stages. International Journal of Agricultural and Biological Engineering, 10(5), 126–133. https://doi.org/10.25165/j.ijabe.20171005.3087

Burgos-Artizzu, X. P., Ribeiro, A., Guijarro, M., & Pajares, G. (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75(2), 337–346. https://doi.org/10.1016/j.compag.2010.12.011

Chawla, P. R., Bajaj, I. B., Survase, S. a., & Singhal, R. S. (2009). Microbial cellulose: Fermentative production and applications ( Review ). Food Technology and Biotechnology, 47(2), 107–124. Retrieved from http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=59853

Donat, W. (2018). Programming with Python : Learn to Program on the World ’ s Most Popular Tiny Computer. APress, California USA. https://doi.org/10.1007/978-1-4842-3769-4

Durand-Petiteville, A., Vougioukas, S., & Slaughter, D. C. (2017). Real-time segmentation of strawberry flesh and calyx from images of singulated strawberries during postharvest processing. Computers and Electronics in Agriculture, 142, 298–313. https://doi.org/10.1016/j.compag.2017.09.011

Iguchi, M., Yamanaka, S., & Budhiono, A. (2000). Bacterial cellulose — a masterpiece of nature ’ s arts. Journal of Materials Science, 35(2), 261–270.

Maharlooei, M., Sivarajan, S., Bajwa, S. G., Harmon, J. P., & Nowatzki, J. (2017). Detection of soybean aphids in a greenhouse using an image processing technique. Computers and Electronics in Agriculture, 132, 63–70. https://doi.org/10.1016/j.compag.2016.11.019

Mohammad, S. M., & Rahman, N. A. (2014). An Overview of Biocellulose Production Using Acetobacter xylinum Culture. 8(6), 307–313. https://doi.org/10.5829/idosi.abr.2014.8.6.1215

Nugroho, D. A., & Aji, P. (2015). Characterization of Nata de Coco Produced by Fermentation of Immobilized Acetobacter xylinum. Agriculture and Agricultural Science Procedia, 3, 278–282. https://doi.org/10.1016/j.aaspro.2015.01.053

Shi, Z., Zhang, Y., Phillips, G. O., & Yang, G. (2014). Utilization of bacterial cellulose in food. Food Hydrocolloids, 35, 539–545. https://doi.org/10.1016/j.foodhyd.2013.07.012

Shilpashree, K., Lokesha, H., & Shivkumar, H. (2015). Implementation of Image Processing on Raspberry Pi. Ijarcce, 4(5), 199–202. https://doi.org/10.17148/IJARCCE.2015.4545



DOI: https://doi.org/10.22146/agritech.49155

Article Metrics

Abstract views : 576 | views : 773

Refbacks

  • There are currently no refbacks.




Copyright (c) 2020 agriTECH

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

agriTECH has been Indexed by:


agriTECH (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.


website statisticsView My Stats