Issue |
ITM Web Conf.
Volume 77, 2025
2025 International Conference on Education, Management and Information Technology (EMIT 2025)
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Article Number | 01021 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/itmconf/20257701021 | |
Published online | 02 July 2025 |
Enhancing tobacco leaf similarity research through multi-modal feature fusion
Kunming University of Science and Technology, College of mechanical and electrical engineering, 650000 Kunming Yunnan, China
* Corresponding author: 2803342872@qq.com
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.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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