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).
[PDF]
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.
