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Related Experiment Video

Updated: Jun 13, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Curvelet Decomposition-Based Tri-Branch Coupling Network for Hyperspectral Unsound Maize Seeds Identification.

Kuibin Zhao^1,2, Lei Lu^1,2,3, Pengtao Lv^1,3

¹College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.

Foods (Basel, Switzerland)

|June 12, 2026

Summary

This study introduces CD-TriMamba, a new framework for classifying maize kernels using hyperspectral and visible-light images. The model achieves high accuracy, improving seed screening and quality evaluation.

Keywords:

Mamba feature curvelet transform information fusion maize identification unsound seeds

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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

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Published on: October 9, 2018

Area of Science:

Agricultural Science
Computer Vision
Data Science

Background:

Accurate and non-destructive classification of maize kernels is crucial for seed screening and quality control.
Current hyperspectral imaging methods using Mamba architectures have limitations in time-frequency analysis and multimodal fusion.
Traditional methods often require extensive spectral preprocessing, potentially introducing errors and reducing model robustness.

Purpose of the Study:

To develop a novel cross-modal classification framework for maize kernel analysis.
To enhance feature extraction and deep fusion by integrating hyperspectral data and visible-light images.
To overcome limitations in existing methods regarding feature fusion and spectral preprocessing.

Main Methods:

Proposed a cross-modal classification framework named CD-TriMamba.
Designed an innovative feature extraction module with Spectral Curvelet Convolution (SCC) for hyperspectral data and Curvelet-Decomposed Convolution (CDC) for spatial modeling.
Implemented a feature rearrangement mechanism and a ConvNeXt-guided tri-branch cross-fusion structure (TriMamba) for deep feature integration.

Main Results:

The CD-TriMamba model achieved outstanding performance in maize kernel seed classification.
Attained an accuracy (Acc) of 99.2% and a Kappa value of 99.1%.
Demonstrated the effectiveness of cross-modal feature fusion for enhanced classification.

Conclusions:

The proposed CD-TriMamba framework effectively integrates hyperspectral and visible-light data for maize kernel classification.
Cross-modal feature fusion significantly improves classification accuracy and robustness.
The model shows strong potential for practical applications in seed screening and quality evaluation.