This work addresses the problem of distinguishing between ripe and unripe watermelons using mobile devices. Through analysing ripeness-related features extracted by thumping watermelons, collecting acoustic signals by microphones on mobile devices, our method can automatically identify the ripeness of watermelons. This is possible in real time, making use of machine learning techniques to provide good accuracy. We firstly collect a training dataset comprising acoustic signals generated by thumping both ripe and unripe watermelons. Audio signal analysis on this helps identify features related to watermelon ripeness. These features are then used to construct a classification model for future signals. Based on this, we developed a crowdsourcing application for Android which allows users to identify watermelon ripeness in real time while submitting their results to us allowing continuous improvement of the classification model. Experimental results show that our method is currently able to correctly classify ripe and unripe watermelons with an overall accuracy exceeding 89 %.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Aizerman A, Braverman E, Rozoner L (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821–837
Ay C (1996) Acoustic evaluation of watermelon internal quality-maturity, cavity existence and orientation. J Agric Mech 5(4):57–71
Baki S, Annuar Mohd ZM, Yassin IM, Hasliza AH, Zabidi A (2010) Non-destructive classification of watermelon ripeness using Mel-frequency cepstrum coefficients and Multilayer Perceptrons. In: Proceedings of international joint conference on neural networks (IJCNN), IEEE, pp 1–6
Balandina E, Trossen D (2006) Nokia remote sensing platform middleware and demo application server: features and user interface. Nokia Research Center, Helsinki
Butterworth S (1930) On the theory of filter amplifiers. Wirel Engineer 7:536–541
Cantwell M (1996) Case study: quality assurance for melons. Perishables Handling Newsl Iss (85):10–12
Diezma-Iglesias B, Ruiz-Altisent M, Barreiro P (2004) Detection of internal quality in seedless watermelon by acoustic impulse response. Biosyst Eng 88(2):221–230
Gouyon F, Pachet F, Delerue O (2000) On the use of zero-crossing rate for an application of classification of percussive sounds. In: Proceedings of the COST G-6 conference on Digital Audio Effects (DAFX-00), Verona, Italy. Helsiniki University of Technology, Helsiniki
Juillerat N, Müller Arisona S, Schubiger-Banz S (2007) Real-time, low latency audio processing in java. In: Proceedings of the international computer music conference, Copenhagen, Denmark
Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th international conference on mobile systems, applications, and services. ACM, Cumberland, pp 165–178
Lu L, Zhang HJ, Li SZ (2003) Content-based audio classification and segmentation by using support vector machines. Multimed Syst 8(6):482–492
McKinney MF, Breebaart J (2003) Features for audio and music classification. In: Proceedings of the third international symposium on music information retrieval (ISMIR), vol 3, pp 151–158
McLoughlin IV (2009) Applied speech and audio processing: with Matlab examples. Cambridge University Press, Cambridge
Misra A, Essl G, Rohs M (2008) Microphone as sensor in mobile phone performance. In: Proceedings of the international conference for new interfaces for musical expression (NIME-08), Genova, Italy
Mitrović D, Zeppelzauer M, Breiteneder C (2010) Features for content-based audio retrieval. Adv Comput 78:71–150
Pohjalainen J (2007) Methods of automatic audio content classification. Ph.D. thesis, Helsiniki University of Technology
Saunders J (1996) Real-time discrimination of broadcast speech/music. In: Proceedings of international conference on acoustics, speech, and signal processing (ICASSP), vol 2, IEEE, pp 993–996
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 Studies 11(2):117–136
This work was established at the Singapore-ETH Centre for Global Environmental Sustainability (SEC), co-funded by the Singapore National Research Foundation (NRF) and ETH Zurich. We would also like to thank developers of open libraries like SVM and FFT used in our implementation. Without their work, we would spend much more time on implementing these algorithms.
About this article
Cite this article
Zeng, W., Huang, X., Müller Arisona, S. et al. Classifying watermelon ripeness by analysing acoustic signals using mobile devices. Pers Ubiquit Comput 18, 1753–1762 (2014). https://doi.org/10.1007/s00779-013-0706-7
- Mobile and physical computing
- Real-time signal analysis
- Machine learning