Prediction of Apple Quality Indicators Under Different Bagging Treatments Using Hyperspectral Imaging Integrated With a Stacking SDAE‐PLSR‐RR Deep Learning Model
Bibliographic record
Abstract
ABSTRACT 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 (R 2 p > 0.84), outperforming full‐spectrum (R 2 p > 0.73) and feature‐wavelength‐based models (R 2 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".