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

作　　者：Lingyu Zhang Laijun Sun Xiuliang Jin Xiangguang Zhao Shujia Li 

机构地区：[1]College of Electronics Engineering,Heilongjiang University,Harbin 150006,Heilongjiang,China [2]Key Laboratory of Crop Physiology and Ecology,Institute of Crop Sciences,Chinese Academy of Agricultural Sciences,Ministry of Agriculture,Beijing 100081,China [3]State Key Laboratory of Crop Gene Resources and Breeding,Institute of Crop Sciences,Chinese Academy of Agricultural Sciences,Beijing 100081,China

出　　处：《The Crop Journal》2025年第2期619-629,共11页作物学报(英文版)

基　　金：supported by Natural Science Foundation of Heilongjiang Province of China(SS2021C005);Province Key Research and Development Program of Heilongjiang Province of China(GZ20220121);the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.

摘　　要：Rapid,accurate seed classification of soybean varieties is needed for product quality control.We describe a 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 fusion attention mechanism module in 2D CNN permits the model to capture local and global feature information by combining with Convolution Block Attention Module(CBAM)and Mobile Vision Transformer(MViT),outperforming conventional hyperspectral image classification models in seed classification.

关 键 词：Soybean seed Classification Deep learning Neural networks Attention mechanisms 

分 类 号：S565.1[农业科学—作物学]