A novel dual-branch spatial-spectral attention fusion model and method: A case study for the detection of nicotine content in tobacco leaves

文献类型： 外文期刊

第一作者： Xing, Fukang

作者： Xing, Fukang;Zhu, Rongguang;Wang, Shichang;Meng, Lingfeng;Dong, Fujia;Bai, Zongxiu;Kang, Yapeng;Xing, Fukang;Zhu, Rongguang;Wang, Shichang;Meng, Lingfeng;Dong, Fujia;Bai, Zongxiu;Kang, Yapeng;Xing, Fukang;Zhu, Rongguang;Wang, Shichang;Meng, Lingfeng;Dong, Fujia;Bai, Zongxiu;Kang, Yapeng;Meng, Lingfeng;Wang, Songfeng;Ren, Jie

作者机构：

关键词： Information fusion; Nicotine; Hyperspectral imaging; Attention mechanism; Transformer

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 236 卷

页码：

收录情况： SCI

摘要： Hyperspectral imaging (HSI) is a powerful tool for crop phenotypic component analysis, but developing efficient collaborative extraction and modeling methods for image and spectral features is a challenge. This study proposed a novel spatial-spectral fusion detection method for nicotine content utilizing hyperspectral imaging (HSI) and deep learning. Spectra of different regions and multi-channel images extracted by two-dimensional correlation analysis (2D-COS) were employed as inputs. A dual-branch spatial-spectral attention fusion model (DSSAM) was developed to enhance the expression ability of different modal information. Among them, two branches designed based on the residual module were used to extract spatial and spectral features, respectively. For the spectral branch, a multi-region spectral attention encoder (MSAE) was added to dynamically adjust the weights of the spectrum across leaf regions. For the spatial branch, a swin window attention (SWA) module was introduced to improve local feature extraction and spatial structure learning. The results demonstrated that MSAE and SWA could improve the spatial-spectral information fusion ability of the DSSAM. Compared with the dual-branch model without the attention modules, the coefficient of determination (R2) and relative prediction deviation (RPD) of the DSSAM model on the test set increased by 7.85% and 2.64%, respectively, and the Root Mean Square Error (RMSE) decreased by 39.29%. In addition, the DSSAM outperformed traditional chemometric and single-modal models, with a R2 of 0.893, a RMSE of 0.289, and a RPD of 3.054. These findings provide a valuable approach for the quality nondestructive detection of cured tobacco leaves and other crop phenotypic components.

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