Enhanced non-destructive of degree of pineapple juiciness using ensemble learning model based on tapping sound sensing

Abstract

This research proposes to enhance the non-destructive method for classifying the juiciness of pineapples using tapping sound sensing. Ten statistical features were extracted from the waveform signals by waveform analysis. These features were then separated into a spectral feature set (3 features) and a temporal feature set (7 features). Each feature set was calculated with the weight of important features and selected features for 15 training datasets using 10 machine learning classifiers. Ten machine learning classifiers were Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Ensemble Voting, Adaboost, Ensemble Bagging, and Ensemble Stacking. The classifiers were evaluated with accuracy and the kappa coefficient. Grid search was used to determine various important hyperparameters for Machine Learning classifiers. The experiment results showed that the Ensemble Voting (soft), Ensemble Stacking, and MLP outperformed other classifiers. They can obtain an accuracy of 92.08%, and kappa coefficients are 0.8811, 0.8808 and 0.8808, respectively.