Deep Learning for Automatic Classification of Carotenoid Associated Color Pigmentation
Issued Date
2024-01-01
Resource Type
ISSN
21593442
eISSN
21593450
Scopus ID
2-s2.0-105000396615
Journal Title
IEEE Region 10 Annual International Conference, Proceedings/TENCON
Start Page
822
End Page
825
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Region 10 Annual International Conference, Proceedings/TENCON (2024) , 822-825
Suggested Citation
Ruaydee K., Kusakunniran W., Srichamnong W. Deep Learning for Automatic Classification of Carotenoid Associated Color Pigmentation. IEEE Region 10 Annual International Conference, Proceedings/TENCON (2024) , 822-825. 825. doi:10.1109/TENCON61640.2024.10902750 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/108600
Title
Deep Learning for Automatic Classification of Carotenoid Associated Color Pigmentation
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Author's Affiliation
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Abstract
This study explores the application of deep learning models, specifically ResNet-34, ResN et-50, and EfficientNet-B0, for the automatic classification of carotenoid-associated color pigmentation in tomatoes. The dataset comprises 250 images categorized into five pigmentation levels, reflecting the varying carotenoid content. Carotenoids, such as lycopene and beta-carotene, are key pigments influencing the color of tomatoes, with deeper reds and oranges indicating higher concentrations. The models were evaluated for direct classification and regression followed by classification. Results show that EfficientNet-B0 achieved the highest accuracy in direct classification (94.00%), while ResNet-34 excelled in regression tasks (91.33%). Future research will continue exploring regression tasks to predict actual carotenoid content in tomatoes, enhancing prediction accuracy and robustness.