Year: 2025
Type: conference-paper
Abstract: 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.
Source: ITM Web of Conferences
Authors: Peiyao Gong, Yuxuan Dong, Kaihu Hou
Institution: Kunming University of Science and Technology
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4411923000