Identification of tomatoes with bruise using laser-light backscattering imaging technique

• Early bruise detection in tomatoes using laser-light backscattering imaging (LLBI). • GLCM was used to extract texture features from tomato backscattering images. • Support vector machine algorithm used to build a model for bruise classification. • LLBI technique achieved 96.111 % accuracy in identifying early bruises in tomatoes. • This innovative LLBI can optimize postharvest quality monitoring of horticulture. Accurately detecting bruise in tomatoes is a significant challenge in postharvest processing due to large-scale production and the demand for high-quality standards. Reduced nutritional value and product appearance due to bruise can affect market value and selling price. Therefore, this study aimed to develop a sensing technology based on laser-light backscattering imaging (LLBI) to identify the bruise of tomatoes. A total of 300 samples were collected and labeled into two main classes namely sound and bruised tomatoes. Each fruit was scanned with various laser wavelengths (450, 532, and 648 nm) at an incident angle of 20° to monitor the structural characteristics. In backscattering image, the gray-level co-occurrence matrix was implemented to extract six texture features including contrast, dissimilarity, homogeneity, energy, angular second moment, and correlation. Additionally, a support vector machine with various kernel functions namely linear, radial basis function (RBF), and polynomial was used to detect bruise. The results showed that based on numerical analysis, the LLBI technique was capable of identifying bruise in tomatoes with an accuracy of 96.111 %. The proposed technique implemented the best combination of laser wavelength and kernel function of 648 nm and RBF, respectively. Therefore, this innovative LLBI technique has the potential to optimize quality monitoring of horticultural products during postharvest handling, reduce fruit rejection rates, and prevent financial losses in the agro-industrial sector.

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Publication Details

Journal
Scientia Horticulturae
Published
2025-07-25
DOI
https://doi.org/10.1016/j.scienta.2025.114301
Citations
2
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
1.50

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article

Identification of tomatoes with bruise using laser-light backscattering imaging technique

Laras Putri Wigati, Fumihiko Tanaka, Muhammad Saukat, Fumina Tanaka et al.
2 citations
Scientia Horticulturae
Spectroscopy and Chemometric Analyses
1.50
article

Identification of tomatoes with bruise using laser-light backscattering imaging technique

Laras Putri Wigati, Fumihiko Tanaka, Muhammad Saukat, Fumina Tanaka, Muhammad Achirul Nanda, Asep Yusuf
article en
2 citations

Abstract

• Early bruise detection in tomatoes using laser-light backscattering imaging (LLBI). • GLCM was used to extract texture features from tomato backscattering images. • Support vector machine algorithm used to build a model for bruise classification. • LLBI technique achieved 96.111 % accuracy in identifying early bruises in tomatoes. • This innovative LLBI can optimize postharvest quality monitoring of horticulture. Accurately detecting bruise in tomatoes is a significant challenge in postharvest processing due to large-scale production and the demand for high-quality standards. Reduced nutritional value and product appearance due to bruise can affect market value and selling price. Therefore, this study aimed to develop a sensing technology based on laser-light backscattering imaging (LLBI) to identify the bruise of tomatoes. A total of 300 samples were collected and labeled into two main classes namely sound and bruised tomatoes. Each fruit was scanned with various laser wavelengths (450, 532, and 648 nm) at an incident angle of 20° to monitor the structural characteristics. In backscattering image, the gray-level co-occurrence matrix was implemented to extract six texture features including contrast, dissimilarity, homogeneity, energy, angular second moment, and correlation. Additionally, a support vector machine with various kernel functions namely linear, radial basis function (RBF), and polynomial was used to detect bruise. The results showed that based on numerical analysis, the LLBI technique was capable of identifying bruise in tomatoes with an accuracy of 96.111 %. The proposed technique implemented the best combination of laser wavelength and kernel function of 648 nm and RBF, respectively. Therefore, this innovative LLBI technique has the potential to optimize quality monitoring of horticultural products during postharvest handling, reduce fruit rejection rates, and prevent financial losses in the agro-industrial sector.

Scientia HorticulturaeVol. 350
Kyushu University (JP), Padjadjaran University (ID)
Universitas Padjadjaran
Zero hunger
Openalex Percentile: Top 17%
Spectroscopy and Chemometric Analyses
1.50
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