Vision-Based Row Detection Algorithms Evaluation for Weeding Cultivator Guidance in Lentil
Bibliographic record
Abstract
It is important to detect crop rows accurately for field navigation. In order to accurate weeding, cultivator guidance system should detect the crop center line precisely. The methods of vision-based row detection for lentil field were studied. Monochrome and color images were used in this research. The color images are transformed into grey scale images in two different formulas to make comparing among them and find an optimal one. In order to detect the center of the crop row rapidly and effectively, Hough transform and gravity center image processing algorithms were applied to acquired images. The field crop images were segmented into two parts by using optimal thresholding (plant and soil as a background), then Hough transform was applied on these binary images. Gray scale images were used in gravity center method. The center line detection algorithms were tested for two weed distribution density, include general and intensive. It was observed that both systems successfully detects and calculates the pose and orientation of the crop row on synthetic images. The mean errors between the calculated and manually estimated lines were obtained. Mean errors for Hough transform and gravity center methods were 8 and 10 mm with standard deviations of 7 and 12 mm in general distribution density and 12 and 16mm with standard deviation of 11 and 15mm in high distribution density, respectively. Computational time for Hough transform and gravity center were 0.7 and 0.4 s for general distribution density and 1.2 and 0.8 s for high distribution density, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".