TY - GEN
T1 - Deep Learning for Automatic Classification of Carotenoid Associated Color Pigmentation
AU - Ruaydee, Kasidit
AU - Kusakunniran, Worapan
AU - Srichamnong, Warangkana
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - carotenoid prediction
KW - classification
KW - deep learning
KW - EfficientNet-B0
KW - regression
KW - ResNet-34
KW - ResNet-50
KW - tomato ripeness
UR - https://www.scopus.com/pages/publications/105000396615
U2 - 10.1109/TENCON61640.2024.10902750
DO - 10.1109/TENCON61640.2024.10902750
M3 - Conference contribution
AN - SCOPUS:105000396615
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 822
EP - 825
BT - Proceedings of the IEEE Region 10 Conference 2024
A2 - Luo, Bin
A2 - Sahoo, Sanjib Kumar
A2 - Lee, Yee Hui
A2 - Lee, Christopher H T
A2 - Ong, Michael
A2 - Alphones, Arokiaswami
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Region 10 Conference, TENCON 2024
Y2 - 1 December 2024 through 4 December 2024
ER -