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Classifying watermelon ripeness by analysing acoustic signals using mobile devices


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 %.

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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.

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Correspondence to Wei Zeng.

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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).

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  • Mobile and physical computing
  • Real-time signal analysis
  • Machine learning
  • Crowdsourcing