Machine learning techniques for classifying the sweetness of watermelon using acoustic signal and image processing

Abstract

Sweetness is an essential factor for assessing the internal quality of fresh watermelon. In this paper, a fusion non-destructive method for classifying watermelon sweetness based on acoustic signal and image processing techniques is proposed. Tapping sound signals, watermelon rind patterns, and weight are considered as features. The application of the three features is inspired by techniques that are used by famers to estimate watermelon maturity. Machine learning (ML) techniques are applied to develop sweetness classification models. Eight classification-based ML techniques are used: Naïve Bayes, K-nearest neighbors, Decision tree, Random forest, Artificial neural network, Logistic regression, Support vector machine, and Gradient boosted trees. The applied ML models are evaluated classification performance using accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic (AUC). The results show that the proposed method can reliably classify watermelon sweetness. The highest classification accuracy achieves 92%, obtained by Gradient boosted trees.