Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform?
Chilli is one of the world’s most widely grown crops. Among all of the chilli variants, C. annuum and C. frustescents are the most prevalent and consistently liked variants in Asia, where it is appreciated for its strong taste and pungency. Nevertheless, harvesting at the proper ripening stage accor...
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| Format: | Article |
| Language: | English |
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International Islamic University Malaysia-IIUM
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113920/ http://psasir.upm.edu.my/id/eprint/113920/1/113920.pdf |
| _version_ | 1848866357337128960 |
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| author | Ibrahim, Zarina Hanafi, Marsyita Shafie, Siti Mariam Syed Ahmad, Sharifah M. |
| author_facet | Ibrahim, Zarina Hanafi, Marsyita Shafie, Siti Mariam Syed Ahmad, Sharifah M. |
| author_sort | Ibrahim, Zarina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Chilli is one of the world’s most widely grown crops. Among all of the chilli variants, C. annuum and C. frustescents are the most prevalent and consistently liked variants in Asia, where it is appreciated for its strong taste and pungency. Nevertheless, harvesting at the proper ripening stage according to their colour, size, and texture is essential to ensure the best quality, marketability, and shelf life. Currently, visual inspection is the primary method used by farmers, which is time-consuming and complicated. Even though automated chilli classification using computer vision and intelligent methods has received scholars’ attention, the classification of C. annuum and C. frustescents ripening stages using deep learning models has not been extensively studied. Hence, this study aims to investigate the effectiveness of three deep learning models, namely EfficientNetB0, VGG16 and ResNet50, in classifying chilli ripening stages into unripe, ripe, and overripe classes. We also introduce a huge dataset comprising 9, 022 images of C. annuum and C. frustescents chilli under various growth stages and imaging conditions which provides sufficient samples for the deep learning modelling. The experimental results show that the ResNet50 model outperforms other models with more than 95% accuracy for all classes. |
| first_indexed | 2025-11-15T14:19:19Z |
| format | Article |
| id | upm-113920 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:19:19Z |
| publishDate | 2024 |
| publisher | International Islamic University Malaysia-IIUM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1139202025-01-13T02:20:05Z http://psasir.upm.edu.my/id/eprint/113920/ Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? Ibrahim, Zarina Hanafi, Marsyita Shafie, Siti Mariam Syed Ahmad, Sharifah M. Chilli is one of the world’s most widely grown crops. Among all of the chilli variants, C. annuum and C. frustescents are the most prevalent and consistently liked variants in Asia, where it is appreciated for its strong taste and pungency. Nevertheless, harvesting at the proper ripening stage according to their colour, size, and texture is essential to ensure the best quality, marketability, and shelf life. Currently, visual inspection is the primary method used by farmers, which is time-consuming and complicated. Even though automated chilli classification using computer vision and intelligent methods has received scholars’ attention, the classification of C. annuum and C. frustescents ripening stages using deep learning models has not been extensively studied. Hence, this study aims to investigate the effectiveness of three deep learning models, namely EfficientNetB0, VGG16 and ResNet50, in classifying chilli ripening stages into unripe, ripe, and overripe classes. We also introduce a huge dataset comprising 9, 022 images of C. annuum and C. frustescents chilli under various growth stages and imaging conditions which provides sufficient samples for the deep learning modelling. The experimental results show that the ResNet50 model outperforms other models with more than 95% accuracy for all classes. International Islamic University Malaysia-IIUM 2024-07-14 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/113920/1/113920.pdf Ibrahim, Zarina and Hanafi, Marsyita and Shafie, Siti Mariam and Syed Ahmad, Sharifah M. (2024) Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? IIUM Engineering Journal, 25 (2). pp. 167-178. ISSN 1511-788X; eISSN: 2289-7860 https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/2769 10.31436/iiumej.v25i2.2769 |
| spellingShingle | Ibrahim, Zarina Hanafi, Marsyita Shafie, Siti Mariam Syed Ahmad, Sharifah M. Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? |
| title | Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? |
| title_full | Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? |
| title_fullStr | Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? |
| title_full_unstemmed | Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? |
| title_short | Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform? |
| title_sort | classification of c. annuum and c. frutescens ripening stages: how well does deep learning perform? |
| url | http://psasir.upm.edu.my/id/eprint/113920/ http://psasir.upm.edu.my/id/eprint/113920/ http://psasir.upm.edu.my/id/eprint/113920/ http://psasir.upm.edu.my/id/eprint/113920/1/113920.pdf |