PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

 

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Recognition of Fruit Types from Striking and Flicking Sounds

Rong Phoophuangpairoj

Pertanika Journal of Science & Technology, Volume 31, Issue 6, October 2023

DOI: https://doi.org/10.47836/pjst.31.6.04

Keywords: Flicking sounds, fruit grading, fruit recognition, Hidden Markov Models, striking

Published on: 12 October 2023

This paper proposes a method to recognize fruits whose quality, including their ripeness, grades, brix values, and flesh characteristics, cannot be determined visually from their skin but from striking and flicking sounds. Four fruit types consisting of durians, watermelons, guavas, and pineapples were studied in this research. In recognition of fruit types, preprocessing removes the non-striking/non-flicking parts from the striking and flicking sounds. Then the sequences of frequency domain acoustic features containing 13 Mel Frequency Cepstral Coefficients (MFCCs) and their 13 first- and 13 second-order derivatives were extracted from striking and flicking sounds. The sequences were used to create the Hidden Markov Models (HMMs). The HMM acoustic models, dictionary, and grammar were incorporated to recognize striking and flicking sounds. When testing the striking and flicking sounds obtained from the fruits used to create the training set but were collected at different times, the recognition accuracy using 1 through 5 strikes/flicks was 98.48%, 98.91%, 99.13%, 98.91%, and 99.57%, respectively. For an unknown test set, of which the sounds obtained from the fruits that were not used to create the training set, the recognition accuracy using 1 through 5 strikes/flicks were 95.23%, 96.82%, 96.82%, 97.05%, and 96.59%, respectively. The results also revealed that the proposed method could accurately distinguish the striking sounds of durians from the flicking sounds of watermelons, guavas, and pineapples.

  • Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., & Soomro, A. B. (2022). A deep learning-based model for date fruit classification. Sustainability, 14(10), Article 6339. https://doi.org/10.3390/ su14106339

  • Asriny, D. M., Rani, S., & Hidayatullah, A. F. (2020). Orange fruit images classification using convolutional neural networks. IOP Conference Series: Materials Science and Engineering, 803(1), Article 012020. https://doi.org/10.1088/1757-899X/803/1/012020

  • Chavan, R. S., & Sable, G. S. (2013). An overview of speech recognition using HMM. International Journal of Computer Science and Mobile Computing, 2(6), 233-238.

  • Elharati, H. A., Alshaari, M., & K√ępuska, V. Z. (2020) Arabic speech recognition system based on MFCC and HMMs. Journal of Computer and Communications, 8(3), 28-34. https://doi.org/10.4236/jcc.2020.83003

  • Fan, S., Li, J., Zhang, Y., Tian, X., Wang, Q., He, X., Zhang, C., & Huang, W. (2020). On line detection of defective apples using computer vision system combined with deep learning methods. Journal of Food Engineering. 286, Article 110102. https://doi.org/10.1016/j.jfoodeng.2020.110102

  • Hatala, Z., & Puturuhu, F. (2021). Viterbi algorithm and its application to Indonesian speech recognition. Journal of Physics: Conference Series, 1752(1), Article 012085. https://doi.org/10.1088/1742-6596/1752/1/012085

  • Hossain, M. S., Al-Hammadi, M., & Muhammad, G. (2018). Automatic fruit classification using deep learning for industrial applications. IEEE Transactions on Industrial Informatics, 15(2), 1027-1034. https://doi.org/10.1109/TII.2018.2875149

  • Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151, Article 107398. https://doi.org/10.1016/j.ymssp.2020.107398

  • Naithani, K., Thakkar, V. M., & Semwal, A. (2018, August 22-24). English language speech recognition using MFCC and HMM. [Paper presentation]. International Conference on Research in Intelligent and Computing in Engineering (RICE), Salvador, El Salvador. https://doi.org/10.1109/RICE.2018.8509046

  • Najkar, N., Razzazi, F., & Sameti, H. (2010). A novel approach to HMM-based speech recognition systems using particle swarm optimization. Mathematical and Computer Modelling, 52(11-12), 1910-1920. https://doi.org/10.1016/j.mcm.2010.03.041

  • Phoophuangpairoj, R. (2013). Determining guava freshness by flicking signal recognition using HMM acoustic models. International Journal of Computer Theory and Engineering, 5(6), 877-884. https://doi.org/10.7763/IJCTE.2013.V5.815

  • Phoophuangpairoj, R. (2014a). Automated classification of watermelon quality using non-flicking reduction and HMM sequences derived from flicking sound characteristics. Journal of Information Science and Engineering, 30(4), 1015-1033.

  • Phoophuangpairoj, R. (2014b). Computerized unripe and ripe durian striking sound recognition using syllable-based HMMs. Applied Mechanics and Materials, 446-447, 927-935. https://doi.org/10.4028/www.scientific.net/amm.446-447.927

  • Phoophuangpairoj, R. (2014c). Durian ripeness striking sound recognition using N-gram models with N-best lists and majority voting. In S. Boonkrong, H. Unger & P. Meesad (Eds), Recent Advances in Information and Communication Technology: Proceedings of the 10th International Conference on Computing and Information Technology (IC2IT2014) (pp. 167-176). Springer

  • Phoophuangpairoj, R., & Srikun, N. (2014). Computerized recognition of pineapple grades using physicochemical properties and flicking sounds. International Journal of Agricultural and Biological Engineering, 7(3), 93-101.

  • Phoophuangpairoj, R. (2020, October 21-22). Recognizing breathing sounds using HMMs and grammar. [Paper presentation]. Proceedings of the 5th International Conference on Information Technology (InCIT2020), ChonBuri, Thailand. https://doi.org/10.1109/InCIT50588.2020.9310966

  • Raja S. L., Ambika, N, Divya, V., & Kowsalya, T, (2018). Fruit classification system using computer vision: A review. International Journal of Trend in Research and Development, 5(1), 22-26.

  • Shahi, T. B., Sitaula, C., Neupane, A., & Guo, W. (2022). Fruit classification using attention-based MobileNetV2 for industrial applications. Plos One 17(2), Article e0264586. https://doi.org/10.1371/journal. pone.0264586

  • Stevner, A. B. A., Vidaurre, D., Cabral, J., Rapuano, K., Nielsen, S. F. V., Tagliazucchi, E., Laufs, H., Vuust, P., Deco, G., Woolrich, M. W., Someren, E. V. & Kringelbach (2019). Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature Communications, 10(1), Article 1035. https://doi.org/10.1038/s41467-019-08934-3

  • Zeng, G. (2017, October 3-5). Fruit and vegetables classification system using image saliency and convolutional neural network. [Paper presentation]. IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China. https://doi.org/10.1109/ITOEC.2017.8122370.

ISSN 0128-7680

e-ISSN 2231-8526

Article ID

JST-3960-2022

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