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Non-destructive classification of melon sweetness levels using segmented rind properties based on semantic segmentation models

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Abstract

Melon is one of the most consumed crops worldwide and has high marketability. Consumers prefer sweet melons. However, the nondestructive determination of melon sweetness is challenging because of its thick rind. In this study, we presented a novel approach for predicting melon sweetness levels using features extracted from segmented rind images and machine learning techniques. We extracted various features from melon rinds images, such as the net density, net thickness, and rind color, using a semantic segmentation model. These features were used as factors in grading melon quality. Experiments on various machine learning models showed that the one-dimensional convolutional neural network model achieved the best performance with 85.71% accuracy, 96.00% precision, and 87.27% F-score. Moreover, It indicated that the sweetness classification performance over a binary class (combining sweet and ‘very sweet’ classes into one class) achieved better result than over multiple classes.

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Data Availability

The melon dataset utilized in this research is accessible and available for download via the following link: https://github.com/codaibk/MelonDataset.

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Acknowledgements

The work was supported by National Science and Technology Council of the Republic of China under grant NSTC 112-2222-E-032-001. The work was also supported by Academia Sinica under grant AS-TP-110-M07. Furthermore, we would like to express our appreciation to Known-You Seed Co. for providing the melon dataset used in our research.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, TamKang University, New Taipei, 251301, Taiwan

    Trang-Thi Ho

  2. University of Science and Technology, Faculty of Transportation Mechanical Engineering, The University of Danang, Danang, 550000, Vietnam

    Thang Hoang

  3. Research Center for Information Technology Innovation, Academia Sinica, Taipei, 10607, Taiwan

    Khoa-Dang Tran & Yennun Huang

  4. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan

    Nguyen Quoc Khanh Le

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Ho, TT., Hoang, T., Tran, KD. et al. Non-destructive classification of melon sweetness levels using segmented rind properties based on semantic segmentation models. Food Measure 17, 5913–5928 (2023). https://doi.org/10.1007/s11694-023-02092-3

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