Random Forests in the Supervised Classification of Multidimensional Images of the Tetrazolium Test
Bibliographic record
Abstract
The quality of the soybean seed can be influenced by several factors that may occur at any stage of production. Mechanical damage, deterioration by humidity and the damage caused by bed bugs are among such problems. The tetrazolium test is adopted by the seed industry, especially for testing soybeans, due to its accuracy, fast result, and the large amount of information it provides. Digital processing and image analysis can be used to aid the extraction and classification of standards for minimizing the subjectivity implicit in the test, thus allowing more credibility to the information. The aim of this work is testing the effectiveness of Random Forests in the supervised classification of soybean embryos images submitted to the tetrazolium test. In order to do so, we used the Trainable Weka Segmentation plugin to perform the segmentation process, and the WEKA software to evaluate the quality of the classifier model obtained. During the process, 222,646 instances among 230,388 instances were correctly classified (96.7%), with Kappa index of 0.95, showing the classifier excellent performance regarding the proposed dataset. The supervised classification, combined with pixel-based segmentation, proved to be efficient in extracting more coherent visual information on seed damage. Also, we conclude that the choice of image attributes, along with the algorithm used in the work, showed to be competent in the classification process of high dimensionality samples.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".