An Imagery-Based Weed Cover Threshold Established Using Expert Knowledge
Bibliographic record
Abstract
The implementation of site-specific weed management requires information about weed cover and decision support systems to determine weed cover thresholds and concomitant herbicide rates. Although it is possible to create accurate weed cover maps over large areas, weed cover thresholds have generally been evaluated using tedious weed density counts. To bridge this gap between weed cover obtained by machine vision and the concept of economic threshold, crop advisers specializing in weed scouting were asked to evaluate over 2,500 weed cover images (2 m by 3 m) and determine if a given image would require herbicide application or not. Using the area under the “receiver operating characteristic” curve method, an optimal weed cover threshold was established. The derived economic thresholds ranged from 0.06 to 0.31% weed cover contingent on the level of tolerance of the expert adviser. Although this threshold seems low, it is comparable with economic threshold values based on weed density.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".