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GMFNet: A GADF-Mamba Fusion Network for Soybean Seed Hyperspectral Classification
Chu Zhang1, Kai Gao1, Xiaoyu Fu1
1School of Information Engineering, Huzhou Normal University, Huzhou 313000, China.
Foods (Basel, Switzerland)
|June 26, 2026
Summary
A new GADF-Mamba Fusion Network (GMFNet) accurately identifies soybean seed cultivars using hyperspectral imaging. This method combines spectral and structural analysis for improved non-destructive quality control in the food industry.
Area of Science:
- Agricultural Science
- Computer Vision
- Spectroscopy
Background:
- Accurate soybean seed cultivar identification is vital for agriculture and food quality control.
- Existing methods struggle with similar spectral profiles, hindering precise classification.
- Developing rapid, non-destructive identification techniques is a key challenge.
Purpose of the Study:
- To propose an effective hyperspectral classification framework for single soybean seeds.
- To address limitations in capturing both spectral sequence and inter-band relationships.
- To enhance non-destructive automated quality control in the food industry.
Main Methods:
- Acquired hyperspectral images of 24,800 soybean seeds from eight cultivars (900-1700 nm).
- Developed a GADF-Mamba Fusion Network (GMFNet) integrating Mamba for spectral sequence and ResNet18 for GADF-based structural features.
- Employed a weighted feature fusion module for final classification.
Main Results:
- Mamba achieved 0.8721 test accuracy on raw spectral data; ResNet18 achieved 0.8737 on GADF images.
- The GMFNet with weighted fusion reached 0.9039 validation and 0.9011 test accuracies.
- Demonstrated high complementarity between spectral sequential and GADF-based structural information.
Conclusions:
- The GMFNet offers a robust hyperspectral solution for single-seed soybean cultivar identification.
- The fusion strategy effectively leverages complementary spectral and structural data.
- The framework shows significant potential for non-destructive automated quality control in food applications.
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