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General Information
    • ISSN: 1793-8201 (Print)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta
    • Executive Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • E-mail: ijcte@iacsitp.com
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2013 Vol.5(6): 877-884 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2013.V5.815

Determining Guava Freshness by Flicking Signal Recognition Using HMM Acoustic Models

Rong Phoophuangpairoj

Abstract—Being able to determine the freshness or quality of fruit automatically is significant because people in the world consume fruit. Countless fruit buyers can be disappointed when they purchase stale, old or sub-standard produce. Studying and developing a computerized method that helps to determine the freshness of fruit without cutting, destroying or tasting is interesting because it could be of benefit to people worldwide. A method using non-flicking reduction preprocessing and acoustic models of different freshness levels is proposed to recognize fresh and not fresh guava flicking signals. In the recognition process, first, the non-flicking parts of the signals are reduced. Then, spectral features of the signals are extracted. Finally, 1) acoustic models are created using Hidden Markov Models (HMM), 2) acoustic sequences of fresh and not fresh guavas are defined and 3) defined possible freshness recognition results are applied to determine guava freshness. The proposed method resulted in average correct freshness recognition rates of 92.00%, 88.00% and 94.00% from fresh, 3 and 6-day-kept guava unknown test sets, respectively. Average correct freshness recognition rates of 90.00%, 90.67%, 92.00%, 92.00% and 92.00% were obtained when using one through five flicks, respectively. An average recognition time of less than 50 milliseconds was taken when using any number of flicks from one to five. The results indicate that the proposed method using three to five flicks is time-efficient and accurate enough to be used to determine the quality of guavas.

Index Terms—Guava, guava freshness, flicking signals, acoustic models, different freshness levels, freshness recognition, HMM.

Rong Phoophuangpairoj is with the Department of Computer Engineering, College of Engineering, Rangsit University, Thailand (e-mail: gamboge@hotmail.com).


Cite:Rong Phoophuangpairoj, "Determining Guava Freshness by Flicking Signal Recognition Using HMM Acoustic Models," International Journal of Computer Theory and Engineering vol. 5, no. 6, pp. 877-884, 2013.

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