Abstract
In this paper, an efficient procedure for ripeness detection of watermelon was presented. A nondestructive method was used based on vibration response to determine the internal quality of watermelon. The responses of samples to vibration excitation were optically recorded by a Laser Doppler (LD) vibrometer. Vibration data was collected from watermelons of two qualities, namely, ripe and unripe. Vibration signals were transformed from time-domain to frequency-domain by fast Fourier transform (FFT). Twenty nine features were extracted from the FFT amplitude and phase angle of the vibration signals. K-nearest neighbor (KNN) analysis was applied as a classifier in decision-making stage. The experimental results showed that the usage of the FFT amplitude of the vibration signals gave the maximum classification accuracy. This method allowed identification at a 95.0 % level of efficiency. Hence, the proposed method can reliably detect watermelon ripeness.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Armstrong PR, Stone ML, Brusewitz GH (1997) Nondestructive acoustic and compression measurements of watermelon for internal damage detection. Appl Eng Agric 13(5):641–645
Beltrán NH, Duarte-Mermoud MA, Bustos MA, Salah SA, Loyola EA, Peña-Neira AI, Jalocha JW (2006) Feature extraction and classification of Chilean wines. J Food Eng 75:1–10
Bengtsson GB, Lundby F, Haugen JE, Egelandsdal B, Marheim JA (2003) Prediction of postharvest maturity and size of Victoria plums by vibration response. Acta Hortic 599:367–372
Chica M, Campoy P (2012) Discernment of bee pollen loads using computer vision and one-class classification techniques. J Food Eng 112:50–59
Choi K, Singh S, Kodali A, Pattipati KR, Sheppard JW, Namburu SM, Chigua S, Prokhorov DV, Qiao L (2007) Novel classifier fusion approaches for fault diagnosis in automotive systems. Autotestcon, Baltimore, pp 260–269
Diezma-Iglesias B, Ruiz-Altisent M, Orihuel B (2002) Acoustic impulse response for detecting hollow heart in seedless watermelon. In Proceedings of Postharvest Unlimited International Conference, Leuven, Belgium
Duda RO, Hart P, Storck DG (2001) Pattern classification, 2nd edn. Wiley, New York
Flores K, Sanchez MT, Perez-Marin DC, Lopez MD, Guerrero JE, Garrido-Varo A (2008) Prediction of total soluble solid content in intact and cut melons and watermelons using near infrared spectroscopy. J Near Infrared Spectrosc 16(2):91–98
Ito H, Morimoto S, Yamauchi R, Ippoushi K, Azuma K, Hugashio H (2002) Potential of near infrared spectroscopy for nondestructive estimation of soluble solids in watermelons. Acta Hortic 588:353–356
Jamal N, Ying Y, Wang J, Rao X (2005) Finite element models of watermelon and their applications. Transactions of the CSAE 21(1):17–22
Kato K (1997) Electrical density sorting and estimation of soluble solids content of watermelon. J Agr Eng Res 67(2):161–170
Landahl S, Terry LA (2012) Avocado firmness monitoring with values obtained by means of laser Doppler vibrometry. Acta Hortic 945:239–245
Lu Z, Szafron D, Greiner R, Lu P, Wishart DS, Poulin B, Anvik J, Macdonell C, Eisner R (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20(4):547–556
Mollazade K, Omid M, Arefi A (2012) Comparing data mining classifiers for grading raisins based on visual features. Comput Electron Agr 84:124–131
Motomura Y, Nagao T, Sakurai N (2004) Nondestructive and noncontact measurement of flesh firmness of 6 apple cultivars by laser Dopplar vibrometer (LDV). J Jpn Soc Food Sci 51(9):483–490
Muramatsu N, Sakurai N, Wada N, Yamamoto R, Takahara T, Ogata T, Tanaka K, Asakura T, Ishikawa-Takano Y, Nevins DJ (1999) Evaluation of fruit tissue texture and internal disorders by laser Doppler detection. Postharvest Biol Tec 15(1):83–88(6)
Muramatsu N, Sakurai N, Wada N, Yamamoto R, Tanaka K, Asakura T, Ishikawa-Takano Y, Nevins DJ (2000) Remote sensing of fruit textural changes with a laser Doppler vibrometer. J Am Soc Hortic Sci 125(1):120–127
Nelson SO, Guo W, Trabelsi S, Kays SJ (2007) Dielectric spectroscopy of watermelons for quality sensing. Meas Sci Technol 18:1887–1892
Omid M, Mahmoudi A, Omid MH (2009) An intelligent system for sorting pistachio nut varieties. Expert Syst Appl 36:11528–11535
Oveisi Z, Minaei S, Rafiee S, Eyvani A, Borghei A (2012) Application of vibration response technique for the firmness evaluation of pear fruit during storage. J Food Sci Technol. doi:10.1007/s13197-012-0811-z
Sakurai N, Iwatani S, Terasaki S, Yamamoto R (2005) Evaluation of ‘Fuyu’ persimmon texture by a new parameter, “Sharpness index”. J Jpn Soc Hortic Sci 74:150–158
Shahabi C, Kolahdouzan MR, Sharifzadeh M (2003) A road network embedding technique for k-nearest neighbor search in moving object databases. GeoInformatica 7(3):255–273
Sone I, Olsen RL, Sivertsen AH, Eilertsen G, Heia K (2012) Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging. J Food Eng 109:482–489
Song Y, Huang J, Zhou D, Zha H, Giles CL (2007) Informative K-nearest neighbor pattern classification. In Proceedings of the eleventh European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, pp 248–264
Stone ML, Armstrong PR, Zhang X, Brusewitz GH, Chen DD (1996) Watermelon maturity determination in the field using acoustic impulse impedance techniques. T ASAE 39(6):2325–2330
Sun T, Huang K, Xu H, Ying Y (2010) Research advances in nondestructive determination of internal quality in watermelon/melon: a review. J Food Eng 100:569–577
Taniwaki M, Hanada T, Sakurai N (2009a) Postharvest quality evaluation of “Fuyu” and “Taishuu” persimmons using a nondestructive vibrational method and an acoustic vibration technique. Postharvest Biol Tec 51(1):80–85
Taniwaki M, Hanada T, Tohro M, Sakurai N (2009b) Non-destructive determination of the optimum eating ripeness of pears and their texture measurements using acoustical vibration techniques. Postharvest Biol Tec 51:305–310
Taniwaki M, Takahashi M, Sakurai N (2009c) Determination of optimum ripeness for edibility of postharvest melons using nondestructive vibration. Food Res Int 42:137–141
Terasaki S, Wada N, Sakurai N, Muramatsu N, Yamamoto R, Nevins DJ (2001) Nondestructive measurement of kiwifruit ripeness using a laser Doppler vibrometer. T ASAE 44:81–87
Terasaki S, Sakurai N, Zebrowski J, Murayama H, Yamamoto R, Nevins DJ (2006) Laser Doppler vibrometer analysis of changes in elastic properties of ripening ‘La France’ pears after postharvest storage. Postharvest Biol Tec 42:198–207
Tollner EW (1993) X-ray technology for detecting physical quality attributes in agricultural produce. Postharvest News Inf 4(6):149–155
Widodo A, Yang BS (2007) Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst Appl 33:241–250
Yamamoto H, Iwamoto M, Haginuma S (1980) Acoustic impulse response method for measuring natural frequency of intact fruits and preliminary applications to internal quality evaluation of apples and watermelons. J Texture Stud 11(2):117–136
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Abbaszadeh, R., Moosavian, A., Rajabipour, A. et al. An intelligent procedure for watermelon ripeness detection based on vibration signals. J Food Sci Technol 52, 1075–1081 (2015). https://doi.org/10.1007/s13197-013-1068-x
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13197-013-1068-x