Potato Sorting Based on Size and Color in Machine Vision System
Bibliographic record
Abstract
Potato (Solanum tuberosum) is cultivated as a major food resource in some countries that have moderate climate. Manual sorting is labor intensive. Furthermore in mechanical sorting the crop damages is high, for this reason we must operate a system in which the crop damages would be diminished. For sorting of potatoes fast, accurate and less labor intensive modern techniques such as Machine vision is created. Machine vision system is one of the modern sorting techniques. The basis of this method is imaging of samples, analysis of the images, comparing them with a standard and finally decision making in acceptance or rejection of samples. In this research 110 numbers of potatoes from Agria variety were prepared. Samples were pre-graded based on quantitative, qualitative and total factors manually before sorting. Quantitative, qualitative and total sorting in Machine vision system was performed by improving images quality and extracting the best thresholds. The accuracy of total sorting was %96.823.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".