Deep learning-enhanced jewelry material jadeite jade quality assessment
Jadeite jade, renowned for its unique texture and cultural significance, stands as the epitome of jade varieties, embodying the latest evolution of China’s jade culture. This research endeavors to establish an AI model for precisely screening jadeite quality, employing deep learning techniques to re...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Springer
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115023/ |
| _version_ | 1848866663843233792 |
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| author | Meng, Liang Raja Ahmad Effendi, Raja Ahmad Azmeer Sun, Wei Mo, Lili Abdul Rahman, Ahmad Rizal Hsu, Yu-Lin Barron, Deirdre |
| author_facet | Meng, Liang Raja Ahmad Effendi, Raja Ahmad Azmeer Sun, Wei Mo, Lili Abdul Rahman, Ahmad Rizal Hsu, Yu-Lin Barron, Deirdre |
| author_sort | Meng, Liang |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Jadeite jade, renowned for its unique texture and cultural significance, stands as the epitome of jade varieties, embodying the latest evolution of China’s jade culture. This research endeavors to establish an AI model for precisely screening jadeite quality, employing deep learning techniques to revolutionize jadeite design and detection. The objective is to provide jewelry companies, designers, and customers with an unbiased means of grading and evaluating jadeite quality. We have meticulously curated a database of jadeite images, applied preprocessing techniques, and have harnessed convolutional neural networks (CNN) for feature extraction. The outcomes were promising, with the model achieving notable performance indicators: an accuracy rate of approximately 84.75%, a recall rate of about 84.94%, and an F1 score of roughly 73.76% in jade image classification tasks. These results underscore the model’s effectiveness in the assessment of jadeite quality. Incorporating computer-aided technology into jadeite screening foreshadows a transforma- tive era where artificial intelligence seamlessly integrates with traditional jade carving design, signifying a pivotal shift in the industry’s landscape. |
| first_indexed | 2025-11-15T14:24:11Z |
| format | Article |
| id | upm-115023 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:24:11Z |
| publishDate | 2024 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1150232025-02-17T07:18:13Z http://psasir.upm.edu.my/id/eprint/115023/ Deep learning-enhanced jewelry material jadeite jade quality assessment Meng, Liang Raja Ahmad Effendi, Raja Ahmad Azmeer Sun, Wei Mo, Lili Abdul Rahman, Ahmad Rizal Hsu, Yu-Lin Barron, Deirdre Jadeite jade, renowned for its unique texture and cultural significance, stands as the epitome of jade varieties, embodying the latest evolution of China’s jade culture. This research endeavors to establish an AI model for precisely screening jadeite quality, employing deep learning techniques to revolutionize jadeite design and detection. The objective is to provide jewelry companies, designers, and customers with an unbiased means of grading and evaluating jadeite quality. We have meticulously curated a database of jadeite images, applied preprocessing techniques, and have harnessed convolutional neural networks (CNN) for feature extraction. The outcomes were promising, with the model achieving notable performance indicators: an accuracy rate of approximately 84.75%, a recall rate of about 84.94%, and an F1 score of roughly 73.76% in jade image classification tasks. These results underscore the model’s effectiveness in the assessment of jadeite quality. Incorporating computer-aided technology into jadeite screening foreshadows a transforma- tive era where artificial intelligence seamlessly integrates with traditional jade carving design, signifying a pivotal shift in the industry’s landscape. Springer 2024 Article PeerReviewed Meng, Liang and Raja Ahmad Effendi, Raja Ahmad Azmeer and Sun, Wei and Mo, Lili and Abdul Rahman, Ahmad Rizal and Hsu, Yu-Lin and Barron, Deirdre (2024) Deep learning-enhanced jewelry material jadeite jade quality assessment. JOM, 77 (1). pp. 211-224. ISSN 1047-4838; eISSN: 1543-1851 https://link.springer.com/article/10.1007/s11837-024-06930-7?error=cookies_not_supported&code=632dcf09-f6ba-4157-96d1-9ff7a2a06007 10.1007/s11837-024-06930-7 |
| spellingShingle | Meng, Liang Raja Ahmad Effendi, Raja Ahmad Azmeer Sun, Wei Mo, Lili Abdul Rahman, Ahmad Rizal Hsu, Yu-Lin Barron, Deirdre Deep learning-enhanced jewelry material jadeite jade quality assessment |
| title | Deep learning-enhanced jewelry material jadeite jade quality assessment |
| title_full | Deep learning-enhanced jewelry material jadeite jade quality assessment |
| title_fullStr | Deep learning-enhanced jewelry material jadeite jade quality assessment |
| title_full_unstemmed | Deep learning-enhanced jewelry material jadeite jade quality assessment |
| title_short | Deep learning-enhanced jewelry material jadeite jade quality assessment |
| title_sort | deep learning-enhanced jewelry material jadeite jade quality assessment |
| url | http://psasir.upm.edu.my/id/eprint/115023/ http://psasir.upm.edu.my/id/eprint/115023/ http://psasir.upm.edu.my/id/eprint/115023/ |