Abstract: This work aimed to address the desire of both melon growers and consumers for convenient, rapid, low-cost and non-destructive detection of Hami melon ripeness. In this paper, a mobile phone was used to record acoustic signals generated by thumping Hami melons with different maturity levels. Then the raw signals were preprocessed to remove noises and segmented into thumping event frames. A series of extraction steps were performed on these acoustic signals, including the calculation of root mean square, start/end detection, extraction of thumping events, filtration with Butterworth filter and fast Fourier transform. As a result, eleven features including short-time energy (STE), frame energy (E), average amplitude difference function (AMDF), short-time energy ratios of four sub-bands (SSTE1, SSTE2, SSTE3 and SSTE4), zero crossing rate (ZCR), spectral centroid (wc), resonant frequency (f) and bandwidth (B) were extracted. The significant differences among these features were analyzed and the ripeness-related features were found. Eight features (i.e. STE, E, SSTE1, SSTE2, SSTE4, ZCR, wc and f) were selected to construct a ripe classifier-based support vector machine (SVM) for distinguishing between unripe and ripe melons. Also, seven features (i.e. AMDF, SSTE1, SSTE2, SSTE3, SSTE4, ZCR, and B) were selected to construct a proper ripe SVM classifier for distinguishing properly ripe from overripe melons. For both SVM classifiers, the algorithm used radial base kernel function to achieve better performance. Single or multiple selected ripeness-related features were used as vector to train the SVM classifiers. We measured the recall, precision, accuracy and F1-Measure by confusion matrix analysis for the two classifiers. The results showed that the classifier trained by the feature vectors wc, E and SSTE1 was the most suitable for disguising unripe from ripe melons. While the classifier trained by the feature vectors ZCR, SSTE2 and SSTE3 was the most suitable to discriminate properly ripe from overripe melons. Furthermore, a prediction model for the soluble solids content of melon was constructed by stepwise multiple regressions with wc, E, SSTE1, SSTE2 and SSTE. Finally, we developed an Android application on the mobile phone, which can correctly classify the melon maturities with an overall accuracy of 90.9%. Its classification performance can be improved by the user feedback. Additionally, it can be used to quantitatively detect the sugar content of melon with a high prediction accuracy.

Key words: ripeness, acoustic signal, support vector machine, smartphone, Hami melon