Enhancing tobacco leaf similarity research through multi-modal feature fusion
The study proposed a method for identifying tobacco leaves with similar quality to stored leaves. It calculated leaf similarity using multi-modal fusion. It extracted near-infrared spectral features with a 1D-CNN network and image features with the correlation function in OpenCV. Then, it applied th...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/08/itmconf_emit2025_01021.pdf |
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| _version_ | 1849425712482615296 |
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| author | Gong Peiyao Dong Yuxuan Hou Kaihu |
| author_facet | Gong Peiyao Dong Yuxuan Hou Kaihu |
| author_sort | Gong Peiyao |
| collection | DOAJ |
| description | The study proposed a method for identifying tobacco leaves with similar quality to stored leaves. It calculated leaf similarity using multi-modal fusion. It extracted near-infrared spectral features with a 1D-CNN network and image features with the correlation function in OpenCV. Then, it applied the attention mechanism feature fusion method to combine these features. The method calculated the similarity between tobacco leaves using Euclidean distance, which allows for the identification of replacement leaves most similar to the target leaves. This approach effectively fulfills the objective of sustaining the cigarette leaf group formula during maintenance by addressing the requirement to identify comparable tobacco leaves in instances where a specific raw material is lacking. |
| format | Article |
| id | doaj-art-d51ff381770f4eca964a09f7e61a792d |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-d51ff381770f4eca964a09f7e61a792d2025-08-20T03:29:40ZengEDP SciencesITM Web of Conferences2271-20972025-01-01770102110.1051/itmconf/20257701021itmconf_emit2025_01021Enhancing tobacco leaf similarity research through multi-modal feature fusionGong Peiyao0Dong Yuxuan1Hou Kaihu2Kunming University of Science and Technology, College of mechanical and electrical engineeringKunming University of Science and Technology, College of mechanical and electrical engineeringKunming University of Science and Technology, College of mechanical and electrical engineeringThe study proposed a method for identifying tobacco leaves with similar quality to stored leaves. It calculated leaf similarity using multi-modal fusion. It extracted near-infrared spectral features with a 1D-CNN network and image features with the correlation function in OpenCV. Then, it applied the attention mechanism feature fusion method to combine these features. The method calculated the similarity between tobacco leaves using Euclidean distance, which allows for the identification of replacement leaves most similar to the target leaves. This approach effectively fulfills the objective of sustaining the cigarette leaf group formula during maintenance by addressing the requirement to identify comparable tobacco leaves in instances where a specific raw material is lacking.https://www.itm-conferences.org/articles/itmconf/pdf/2025/08/itmconf_emit2025_01021.pdf |
| spellingShingle | Gong Peiyao Dong Yuxuan Hou Kaihu Enhancing tobacco leaf similarity research through multi-modal feature fusion ITM Web of Conferences |
| title | Enhancing tobacco leaf similarity research through multi-modal feature fusion |
| title_full | Enhancing tobacco leaf similarity research through multi-modal feature fusion |
| title_fullStr | Enhancing tobacco leaf similarity research through multi-modal feature fusion |
| title_full_unstemmed | Enhancing tobacco leaf similarity research through multi-modal feature fusion |
| title_short | Enhancing tobacco leaf similarity research through multi-modal feature fusion |
| title_sort | enhancing tobacco leaf similarity research through multi modal feature fusion |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/08/itmconf_emit2025_01021.pdf |
| work_keys_str_mv | AT gongpeiyao enhancingtobaccoleafsimilarityresearchthroughmultimodalfeaturefusion AT dongyuxuan enhancingtobaccoleafsimilarityresearchthroughmultimodalfeaturefusion AT houkaihu enhancingtobaccoleafsimilarityresearchthroughmultimodalfeaturefusion |