Non-destructive classification of watermelon ripeness using Mel-Frequency Cepstrum Coefficients and Multilayer Perceptrons

We present a non-destructive watermelon classification method using Mel-Frequency Cepstrum Coefficients (MFCC) and Multi-Layer Perceptron (MLP) neural network. Acoustic signals were collected from thumping noises of ripe and unripe watermelon fruits. MFCC was then used to convert the signals into MFCC coefficients. The coefficients were then used to train a MLP, and the MLP gives the final decision on the watermelon ripeness state. In our paper, we describe the methods used to obtain the acoustic samples, as well as the evaluation of several MLP structures and parameters to obtain the best MLP classifier. Our results show that the proposed method was able to discriminate between ripe and unripe watermelons with 77.25% accuracy.