Spectral super-resolution for high-accuracy rice variety classification using hybrid CNN-Transformer model

文献类型： 外文期刊

第一作者： Guo, Chaohui

作者： Guo, Chaohui;Weng, Shizhuang;Zheng, Shouguo;Guo, Chaohui;Zhu, Gongqin;Tu, Debao;Xu, Jianpeng;Zheng, Shouguo

作者机构：

关键词： Spectral super-resolution; Rice variety classification

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.6； 五年影响因子：4.6 ）

ISSN： 0889-1575

年卷期： 2025 年 137 卷

页码：

收录情况： SCI

摘要： Rice variety classification is crucial for ensuring the purity and quality of rice production. In this study, hyperspectral images (HSI) of rice varieties were reconstructed using a spectral super-resolution technique, serving as a basis for an enhanced classification process. RGB images of various rice varieties were utilized to reconstruct the HSI, and a hybrid CNN-Transformer model featuring an efficient feature fusion module aimed at reducing redundancy was developed. Building on the spectral super-resolution model, a Swin-Transformer model was employed for rice variety classification, chosen for its capacity to effectively handle high-dimensional data with fewer parameters and lower FLOPs. The classification accuracy based on actual HSI reached an impressive 99.961 %, while the accuracy based on reconstructed HSI was a close 99.413 %. These results demonstrate that the spectral super-resolution method not only effectively reconstructs HSI images of rice varieties but also supports highly reliable classification. It is indicated by the study that spectral super-resolution can significantly outperform advanced CNN and Transformer-based methods in terms of accuracy and computational efficiency, offering a promising approach for precise rice variety classification and potentially other agricultural applications.

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