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HyperVision-HSI: a classification-guided spectral-spatial decoupling framework for adaptive multi-category fruit SSC

Tongtong Dong1, Dongpo Wei1, Longjie Li2

  • 1College of Mechanical Engineering, Shandong Huayu University of Technology, Dezhou, China.

Frontiers in Plant Science
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

HyperVision-HSI accurately detects fruit sweetness using hyperspectral imaging (HSI). This framework overcomes challenges in multi-category fruit analysis, enabling precise soluble solids content (SSC) prediction.

Keywords:
cross-species sugar content predictiondynamic ROI localizationhyperspectral imagingnondestructive detectionspectral-spatial decoupling

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Computer Vision

Background:

  • Non-destructive detection of soluble solids content (SSC) in fruits is crucial for quality assessment.
  • Hyperspectral imaging (HSI) faces challenges with dynamic feature region distortion and optical heterogeneity across fruit categories.
  • Existing methods struggle with diverse fruit types due to feature dilution.

Purpose of the Study:

  • To develop an adaptive framework (HyperVision-HSI) for accurate, non-destructive SSC detection in multi-category fruits.
  • To address challenges of dynamic ROI localization and inter-species optical heterogeneity in HSI.
  • To enable automated, species-specific quality assessment for diverse agricultural products.

Main Methods:

  • Proposed HyperVision-HSI: a classification-guided spectral-spatial adaptive decoupling framework.
  • Integrated dynamic ROI localization, dual-channel spectral calibration, and category-aware model invocation.
  • Employed species-adaptive thresholds and invoked specialized regression models (KRR, BRR, Lasso) for grapes, tomatoes, and Xiangli pears.

Main Results:

  • Achieved low SSC prediction errors: 0.62°Brix (grapes), 0.32°Brix (tomatoes), 0.37°Brix (Xiangli pears) on independent test sets.
  • Single-frame processing time under 1 second, demonstrating high efficiency.
  • Successfully addressed feature dilution and optical heterogeneity in multi-category fruit analysis.

Conclusions:

  • HyperVision-HSI provides a robust, scalable solution for automated multi-category fruit phenomics.
  • The framework offers a rigorous adaptive technical pathway for diverse agricultural sensing applications.
  • Demonstrated the effectiveness of adaptive decoupling for precise HSI-based quality prediction.