DAFFnet: Seed classification of soybean variety based on dual attention feature fusion networks

文献类型： 外文期刊

第一作者： Zhang, Lingyu

作者： Zhang, Lingyu;Sun, Laijun;Li, Shujia;Jin, Xiuliang;Zhao, Xiangguang

作者机构：

关键词： Soybean seed; Classification; Deep learning; Neural networks; Attention mechanisms

期刊名称：CROP JOURNAL （ 影响因子：5.6； 五年影响因子：6.0 ）

ISSN： 2095-5421

年卷期： 2025 年 13 卷 2 期

页码：

收录情况： SCI

摘要： Rapid, accurate seed classification of soybean varieties is needed for product quality control. We describea hyperspectral image-based deep-learning model called Dual Attention Feature Fusion Networks(DAFFnet), which sequentially applies 3D Convolutional Neural Network (CNN) and 2D CNN. A fusionattention mechanism module in 2D CNN permits the model to capture local and global feature informa-tion by combining with Convolution Block Attention Module (CBAM) and Mobile Vision Transformer(MViT), outperforming conventional hyperspectral image classification models in seed classification.(c) 2025 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting byElsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/

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