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

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

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 a...

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Bibliographic Details
Main Authors: Lingyu Zhang, Laijun Sun, Xiuliang Jin, Xiangguang Zhao, Shujia Li
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-04-01
Series:Crop Journal
Subjects:
Soybean seed
Classification
Deep learning
Neural networks
Attention mechanisms
Online Access:http://www.sciencedirect.com/science/article/pii/S2214514125000224
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Summary: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.
ISSN:2214-5141

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