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An improved 3D-SwinT-CNN network to evaluate the fermentation degree of black tea

文献类型： 外文期刊

作者： Zhu, Fengle 1 ; Wang, Jian 1 ; Zhang, Yuqian 1 ; Shi, Jiang 2 ; He, Mengzhu 3 ; Zhao, Zhangfeng 1 ;

作者机构： 1.Zhejiang Univ Technol, Coll Mech Engn, Hangzhou 310023, Peoples R China

2.Chinese Acad Agr Sci, Tea Res Inst, 9 Meiling South Rd, Hangzhou 310008, Peoples R China

3.Hangzhou City Univ, Sch Comp & Comp Sci, Hangzhou 310015, Peoples R China

关键词： Black tea fermentation; Hyperspectral imaging; 3D-SwinT-CNN; 3D convolutional neural networks; Swin transformer

期刊名称：FOOD CONTROL （ 影响因子：5.6； 五年影响因子：5.4 ）

ISSN： 0956-7135

年卷期： 2025 年 167 卷

页码：

收录情况： SCI

摘要： Fermentation is a key process in forming the flavor quality of black tea. Evaluating the degree of fermentation during black tea processing is difficult. This paper proposes an improved 3D-SwinT-CNN network for the end-toend processing of black tea's hyperspectral images to evaluate the degree of fermentation. The model incorporates dilated convolution and a shifted window self-attention mechanism, expanding the network's receptive field and capturing both local spatial-spectral and global features of black tea hyperspectral images. The accuracy is 98.13% on the test set. Compared to manual feature extraction methods, the accuracy improved by 3.45%. Compared to baseline 3D-CNNs, 3D-SwinT-CNN demonstrates superior spatial-spectral feature extraction, achieving an average accuracy improvement of 10.17%. Ablation experiments were conducted to further verify the effectiveness of the introduced modules. The proposed 3D-SwinT-CNN aims to establish a research foundation for online evaluation of black tea's fermentation degree.

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