Classification and Pixel-Based Segmentation to Evaluate Soybean Seeds Submitted to Tetrazolium Test
Bibliographic record
Abstract
Production and use of high quality seeds are essential for the soybean crop. Thus, the quality control system in seeds industry must be reliable, precise, and fast. Tetrazolium test evaluates not only seeds viability but also their vigor, as well as provides information concerning agents that cause their quality reduction. Although this test does not use expensive devices and reagents, it requires a well-trained analyst. Its precision depends on knowledge of all techniques and required procedures. Besides, also necessary is the observer’s subjectivity. So, this trial aimed at developing a computational tool that could minimize the implicit subjectivity in carrying out this test. It also contributes to generate a greater credibility of information and to guarantee precise answers. Algorithms of supervised classification were applied based on extraction of digital images characterization of tetrazolium test. This procedure aimed at producing pixel-based segmentation of those images, to produce a digital segmented image of tetrazolium test according to damage classes. This tool allows, based on image of tetrazolium test, to identify damage on soybean embryos, as well as its site and extension on tissues, so that the interpretation is less subjective. The applied method allowed identifying damage on images of tetrazolium tests in a straightforward way, as well as extracting safer information about those damages and carrying out management control of tetrazolium test according to a seed data file.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".