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ORBMO-RF: a non-destructive classification method for ginseng seeds based on multimodal fusion and improved
Mingxuan Xue1,2, Yanan Zhu2, Bin Liu2
1School of Information Technology, Jilin Agricultural University, Changchun, China.
Frontiers in Plant Science
|January 30, 2026
Summary
This study introduces a smart method for classifying ginseng seeds using fused image and spectral data. The advanced model significantly improves classification accuracy, aiding in ginseng breeding and quality control.
Area of Science:
- Agricultural Science
- Computer Science
- Biotechnology
Background:
- Precise classification of ginseng seeds is crucial for production, quality, breeding, and industry modernization.
- Current automated precision classification technologies for ginseng seeds are underdeveloped, facing technical bottlenecks.
Purpose of the Study:
- To develop an innovative smart classification method for ginseng seeds using multimodal data fusion.
- To enhance the accuracy and efficiency of ginseng seed classification for industrial applications.
Main Methods:
- A multimodal data fusion approach combining image-derived morphological features and hyperspectral data.
- Recursive Feature Elimination (RFE) for morphological feature selection and Competitive Adaptive Reweighted Sampling (CARS) for spectral band extraction.
- A Random Forest (RF) classification model optimized by an enhanced Red-Billed Blue Magpie Optimization (RBMO) algorithm with hybrid mechanisms for improved convergence.
Main Results:
- The proposed multimodal fusion model demonstrated significant improvements over the baseline RF model.
- Achieved percentage increases of 4.69% in accuracy, 4.79% in precision, 4.69% in recall, and 4.74% in F1-score on test datasets.
Conclusions:
- The developed system provides a robust theoretical and technical foundation for large-scale ginseng seed classification.
- This intelligent decision-making paradigm is transferable for non-destructive testing in traditional Chinese medicine.
Keywords:
ginseng seedsmultimodal fusionnon-destructive classificationrandom forestred-billed blue magpie optimization algorithmMore Related Videos
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