An intelligent procedure for watermelon ripeness detection based on vibration signals.

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.