Deep learning-based method for real-time monitoring of visual attributes during fluidized bed drying
Bibliographic record
Abstract
The color, texture and size of any dried food product could either attract or dissuade potential consumers from purchasing them.This research focused on developing an intelligent system for monitoring real-time changes in visual attributes of green peas during fluidized bed drying.This system was developed using the U-Net and Xception deep learning models.The results were compared to those produced using a classical computer vision method.The deep learning (DL) approach significantly outperformed the classical method both in real-time image segmentation and visual attribute prediction.The Mean Intersection-Over-Union for U-Net and the classical model were 0.9308 and 0.8190, respectively.Using the DL approach, a* and b* color indices were the best indictors for color, homogeneity was the best for surface texture, while equivalent diameter, ferret diameter, filled area and perimeter produced a smoother trend for monitoring product size.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".