DAFFnet: Seed classification of soybean variety based on dual attention feature fusion networks
文献类型： 外文期刊
作者： Zhang, Lingyu 1 ; Sun, Laijun 1 ; Jin, Xiuliang 2 ; Zhao, Xiangguang 3 ; Li, Shujia 1 ;
作者机构： 1.Heilongjiang Univ, Coll Elect Engn, Harbin 150006, Heilongjiang, Peoples R China
2.Chinese Acad Agr Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Inst Crop Sci, Beijing 100081, Peoples R China
3.Chinese Acad Agr Sci, Inst Crop Sci, State Key Lab Crop Gene Resources & Breeding, Beijing 100081, Peoples R China
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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