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February 24, 2026Open Access

ViaNet: Interpretable and Lightweight Deep Hyperspectral Classification of Pepper Seed Viability

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Authors

LZLei ZhuYZYeminzi ZhouYZYueming Zhu

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Overview

Framework classifies pepper seed viability using hyperspectral data, suggesting a non-destructive assessment method.

Key Points

  • To develop a lightweight and interpretable deep learning framework for assessing pepper seed viability through hyperspectral data.
  • Developed ViaNet, a 1D-CNN architecture incorporating SPA and ECA techniques.
  • Utilized 1038 naturally aged pepper seeds with 14-day germination tests data.
  • Applied binary classification of ROI-averaged hyperspectral reflectance vectors for seed viability.
  • Achieved 79.75% recall for germinable seeds.
  • Outperformed traditional machine learning methods in classification accuracy.
  • Highlighted spectral bands linked to biochemical changes during natural aging.

Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/699d4028de8e28729cf653abhttps://doi.org/10.3390/agriculture16040486
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