Prediction of Apple Quality Indicators Under Different Bagging Treatments Using Hyperspectral Imaging Integrated With a Stacking SDAE‐PLSR‐RR Deep Learning Model

The color indices (L*, a*, and b*) and soluble solids content (SSC) serve as essential quality indicators for apples, yet conventional destructive detection methods lack the efficiency required for rapid sorting of apples with varied bagging treatments. To address this limitation, this study proposes a novel stacking model, termed SDAE-PLSR-RR, which integrates hyperspectral imaging with deep learning. Hyperspectral imaging comprehensively captured spectral-spatial features from 307 Fuji apples subjected to three bagging treatments (non-bagged, mesh-bagged, and paper-bagged), enabling systematic analysis of quality-related characteristics. The SDAE-PLSR-RR employs a stacked structure where two parallel, base-level expert models capture complementary features: one Partial Least Squares Regression (PLSR) model processes linear trends in original wavelengths data, while the other analyzes non-linear deep features from a Stacked Denoising Autoencoder (SDAE). A top-level Ridge Regression (RR) model then acts as a meta-learner to fuse the predictions from these two base models, generating a final, more robust output. The integrated SDAE-PLSR-RR model achieved enhanced prediction accuracy for all quality indicators (R² _p > 0.84), outperforming full-spectrum (R² _p > 0.73) and feature-wavelength-based models (R² _p > 0.75). The experimental findings validated the applicability and efficacy of integrating hyperspectral imaging systems with neural network models for non-destructive detection of the quality indicators of apples with different bagging treatments.