Evaluation of segmentation methods for RGB colour image-based detection of Fusarium infection in corn grains using support vector machine (SVM) and pre-trained convolution neural network (CNN)
Bibliographic record
Abstract
This study evaluated six segmentation methods (clustering, flood-fill, graph-cut, colour-thresholding, watershed, and Otsu’s-thresholding) for segmentation accuracy and classification accuracy in discriminating Fusarium infected corn grains using RGB colour images. The segmentation accuracy was calculated using Jaccard similarity index and Dice coefficient in comparison with the gold standard (manual segmentation method). Flood-fill and graph-cut methods showed the highest segmentation accuracy of 77% and 87% for Jaccard and Dice evaluation metrics, respectively. Pre-trained convolution neural network (CNN) and support vector machine (SVM) were used to evaluate the effect of segmentation methods on classification accuracy using segmented images and extracted features from the segmented images, respectively. The SVM based two-class model to discriminate healthy and Fusarium infected corn grains yielded the classification accuracy of 84%, 79%, 78%, 74%, 69% and 65% for graph-cut, watershed, clustering, flood-fill, colour-thresholding, and Otsu’s-thresholding, respectively. In pretrained CNN model, the classification accuracies were 93%, 88%, 87%, 84%, 61% and 59% for flood-fill, graph-cut, colour-thresholding, clustering, watershed, and Otsu’s-thresholding, respectively. Jaccard and Dice evaluation metrics showed the highest correlation with the pretrained CNN classification accuracies with R2 values of 0.9693 and 0.9727, respectively. The correlation with SVM classification accuracies were R2–0.505 for Jaccard and R2–0.5151 for Dice evaluation metrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".