Super learner ensemble-based internal quality assessment of watermelon via integration of tapping acoustics and rind texture analysis
Issued Date
2026-01-01
Resource Type
ISSN
20748523
Scopus ID
2-s2.0-105033891297
Journal Title
International Journal of Advances in Soft Computing and Its Applications
Volume
18
Issue
1
Start Page
79
End Page
97
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Advances in Soft Computing and Its Applications Vol.18 No.1 (2026) , 79-97
Suggested Citation
Chawgien K., Kiattisin S. Super learner ensemble-based internal quality assessment of watermelon via integration of tapping acoustics and rind texture analysis. International Journal of Advances in Soft Computing and Its Applications Vol.18 No.1 (2026) , 79-97. 97. doi:10.15849/ijasca.v18i1.9 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115964
Title
Super learner ensemble-based internal quality assessment of watermelon via integration of tapping acoustics and rind texture analysis
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Author's Affiliation
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Abstract
Watermelon (Citrullus lanatus) is a widely cultivated fruit recognized for its high sugar content. Accurate detection of maturity and soluble solid content (SSC) is essential to ensure optimal harvest timing, sweetness, and market value, as well as to manage resource usage efficiently. This study introduces a low-cost, portable, and non-destructive approach for maturity classification and SSC estimation in Kinnaree watermelon by integrating tapping acoustics and rind texture analysis with ensemble learning algorithms. Tapping-induced acoustic signals were analyzed to extract key resonant features, while rind texture was quantified using image processing techniques. Selected features from both data sources, combined with watermelon mass, were utilized for three-class maturity classification and SSC regression modeling. Machine learning (ML) algorithms were used to map complex and nonlinear relationships between features and watermelon quality attributes. Results demonstrated that acoustic features and fruit mass were critical for maturity classification. Visual features were essential for SSC estimation. Super learner ensemble demonstrates superior predictive accuracy compared to other models, both in classifying ripeness and predicting the SSC of watermelons. Comparative studies with earlier methods confirmed the effectiveness and competitiveness of the proposed technology for non-destructive evaluation of watermelon quality.