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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.

Keywords:

GADF deep learning feature fusion hyperspectral imaging seed classification

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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.