Optimization of a Coffee Bean Roasting Machine Using Fuzzy Logic and Deep Learning Approaches
Bibliographic record
Abstract
Coffee is a primary commodity in international trade.Post-harvest processing, particularly the roasting stage, plays a critical role in determining the final product attributes, such as flavour, aroma, colour, and bioactive compound content.Precise control during the roasting process is essential to ensure quality consistency, especially at a commercial production scale.This study aims to develop an adaptive control system to achieve a uniform roast level in coffee beans.The implemented method integrates fuzzy logic with a deep learning-based evaluation mechanism.The fuzzy logic functions as the main controller for the roaster, dynamically regulating temperature, time, and heat intensity parameters based on sensor input.Subsequently, a Convolutional Neural Network (CNN) algorithm was employed as an objective evaluation system to classify the roast degree (light, medium, dark) based on images of the coffee beans.The research dataset, comprising 1,600 images of roasted coffee beans, was obtained from Kaggle.com for model training and validation, while the beans roasted by the machine were used as test data.The test results demonstrated highly reliable system performance.The fuzzy controller exhibited robust adaptability across various baking phases, and the CNN model achieved a validation accuracy of 95.83% based on the results of 5-fold cross-validation testing.These findings confirm that the integration of these two technologies successfully creates a closed-loop system capable of producing roasted coffee beans with a high degree of consistency and accuracy.This approach also significantly reduces reliance on manual assessment, which is prone to subjectivity and error.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".