273Chapter 12 A hybrid quantum-classical approach for fruit classification and calorie prediction using machine learning
Bibliographic record
Abstract
In the evolving field of modern agriculture, the integration of machines and deep learning deep learning techniques, particularly for fruit classification fruit classification , has become increasingly significant due to the diverse characteristics of fruits. Our research makes a substantial contribution by introducing a high-quality dataset dataset of fruit images and conducting numerical experiments to train neural networks for fruit detection. This study explores the motivations behind focusing on fruit classification and the practical applications of the developed classifiers. We evaluated the effectiveness of various models, including convolutional neural networks (CNN) convolutional neural network , ResNet50 ResNet50 , VGG19, and DenseNet201 DenseNet201 , by measuring their performance in terms of loss and accuracy accuracy on both test and validation datasets. The CNN convolutional neural network model outperformed the others, achieving a test loss of 0.1580 and an accuracy of 96.63%, alongside a validation loss of 0.1020 and an accuracy of 97.15%. ResNet50, VGG19, and DenseNet201 also demonstrated promising results, though slightly less accurate than CNN convolutional neural network . These findings underscore the efficacy of these models in precisely categorizing fruits and quantifying their caloric content, signifying substantial progress in agricultural technology and food science. Additionally, the hybrid quantum-classical model hybrid quantum-classical model yielded encouraging outcomes for both objectives, achieving an overall accuracy of 98.2%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.068 | 0.022 |
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".