Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
A CNN-ELM Classification Model for Automated Tomato Maturity Grading
Dublin Core
Title
Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
A CNN-ELM Classification Model for Automated Tomato Maturity Grading
A CNN-ELM Classification Model for Automated Tomato Maturity Grading
Subject
automated tomato maturity grading; CNN-ELM; convolutional neural
networks; extreme learning machines; hybrid classification model; tomato classification.
networks; extreme learning machines; hybrid classification model; tomato classification.
Description
Abstract. Tomatoes are popular around the world due to their high nutritional value. Tomatoes are also one of the world’s most widely cultivated and profitable crops. The distribution and marketing of tomatoes depend highly on their quality. Estimating tomato ripeness is an essential step in determining shelf life and quality. With the abundant supply of tomatoes on the market, it is exceedingly difficult to estimate tomato ripeness using human graders. To address this issue and improve tomato quality inspection and sorting, automated tomato maturity classification models based on different features have been developed. However,
current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes
a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had
a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data.
current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes
a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had
a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data.
Creator
John Paul Tan Yusiong
Source
DOI: 10.5614/itbj.ict.res.appl.2022.16.1.2
Publisher
IRCS-ITB
Date
01 Desember 2021
Contributor
Sri Wahyuni
Rights
ISSN: 2337-5787
Format
PDF
Language
English
Type
Text
Coverage
Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
Files
Collection
Citation
John Paul Tan Yusiong, “Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
A CNN-ELM Classification Model for Automated Tomato Maturity Grading,” Repository Horizon University Indonesia, accessed March 9, 2025, https://repository.horizon.ac.id/items/show/3441.
A CNN-ELM Classification Model for Automated Tomato Maturity Grading,” Repository Horizon University Indonesia, accessed March 9, 2025, https://repository.horizon.ac.id/items/show/3441.