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Multi-source hierarchical deep learning framework for fine-grained tea classification using hyperspectral imaging
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DOI：10.1016/j.atech.2026.101844.png)
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Abstract
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• Multi-source HSI framework integrates data, feature, and model fusion for classification. • Spectral fusion boosts accuracy for fine-grained tea germplasm classification. • Hybrid1D2D+Transformer and 2DCNNViT improve classification at fine and image levels. • Hybrid1D2D+Transformer reaches 96.87 %, 2DCNNViT achieves 98.95 % and 96.88 % on VI/NI. • Hybrid models extract multi-scale features through local and global context learning.
Keywords:
Hyperspectral imaging
Multi-level fusion
Tea classification
Deep learning
Hybrid models
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