Non-destructive acoustic screening of pineapple ripeness by unsupervised machine learning and Wavelet Kernel methods

Abstract

In a pineapple exporting factory, manual lines are usually built to screen fruits of non-ripen hitting sounds from millions of undecided fruits for long-haul transportation. However, human workers cannot concentratedly listen and make consistent judgments over long hours. Pineapple screening becomes arbitrary after approximately an hour. We developed a non-destructive screening device aside from the conveyor sorter to classify pineapples automatically. The device makes intelligent judgments by tapping a sound source to the skin of pineapples and analyzing the penetrated sounds by wavelet kernel decomposition and unsupervised machine learning (ML). The sound tapping relies on the well-touch of the skin. We also design several acoustic couplers to adapt the vibrator to the skin and pick high-quality penetrated sounds. A Taguchi experiment design was used to determine the most suitable coupler. We found that our unsupervised ML method achieves 98.56% accuracy and 0.93 F1-score by using a specially designed thorn-board for assisting tapping sound to fruit skin.