Design of Tobacco Leaves Classifier Through Fuzzy Clustering-Based Neural Networks With Multiple Histogram Analyses of Images
Bibliographic record
Abstract
This article is concerned with designing a tobacco leaves classifier through fuzzy clustering-based neural networks, which leverage multiple histogram analyses of images. The key issue of the study is to recognize high-quality and low-quality tobacco leaves only by using color images obtained from real industrial areas. This study applies multiple histogram analyses from different color spaces as image preprocessing to extract the meaningful features from high-resolution images. Dimensionality reduction is performed through principal component analysis to extract essential features to reduce model complexity and alleviate overfitting problems. In a classifier, we apply fuzzy clustering-based neural networks that incorporate fuzzy clustering techniques, especially fuzzy C-means clustering, along with a cross-entropy loss function and its learning mechanism. The process of setting and training the membership function of node in the hidden layer is substituted with fuzzy C-means clustering. Also, Softmax function produces the model's output in terms of class probabilities. The cost function of the networks is determined using the cross-entropy loss function, while the learning process involves Newton's method-based iterative nonlinear least square error estimation. The experiment validates the competitiveness of the proposed design methodology using real tobacco images obtained from the industry. The performance of the proposed classifier is compared against other classifiers previously reported in the literature to demonstrate its effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".