文献类型： 外文期刊

作者： Yang, Xu ¹ ; Chen, Yihan ¹ ; Song, Shaozhong ² ; Zhang, Zhimin ³ ;

作者机构： 1.Changchun Univ Sci & Technol, Sch Elect Informat Engn, Changchun, Peoples R China

2.Jilin Engn Normal Univ, Sch Data Sci & Artificial Intelligence, Changchun, Peoples R China

3.Jilin Acad Agr Sci, Peanut Inst, Gongzhuling, Jilin, Peoples R China

关键词： artificial intelligence; sorghum seed; variety identification; multi-modal fusion; ResNet models

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Introduction Sorghum is an important food and feed crop. Identifying sorghum seed varieties is crucial for ensuring seed quality, improving planting efficiency, and promoting sustainable agricultural development.Methods This study proposes a high-precision classification method based on the fusion of RGB images and hyperspectral data, using an improved deep residual convolutional neural network. A spectrogram fusion dataset containing 12,800 seeds from eight sorghum varieties was constructed. The network was enhanced by integrating depthwise separable convolution (DSC) and the Convolutional Block Attention Module (CBAM) into the ResNet50 framework.Results The CBAM-ResNet50-DSC model demonstrated outstanding performance, achieving a classification accuracy of 94.84%, specificity of 99.20%, recall of 94.39%, precision of 94.52%, and an F1-score of 0.9438 on the fusion dataset.Discussion These results confirm that the proposed model can accurately and non-destructively classify sorghum seed varieties. The method offers a dependable and efficient approach for seed screening and has practical value in agricultural applications.