CLASSIFICATION OF WEED PATCHES IN QUICKBIRD IMAGES: VERIFICATION BY GROUND TRUTH DATA
Bibliographic record
Abstract
Current methods for mapping weeds in arable land include manual sampling approaches and online computer-based methods with special sensors. Both methods are expensive, time consuming and not suitable for constructing regional maps of weed status. This study investigated the use of a visual interpretation of high-resolution satellite images from the QuickBird satellite in order to detect weeds in a field of sugar beets (Beta vulgaris L.) near Bonn in Germany. The study compared this visual interpretation with the data acquired applying a WeedScanner survey of the same area. This method allows an exhaustive survey of weeds in the field. The analysis showed that dense clumps of Canada thistle (Cirsium arvense L.) were accurately detected in the satellite images, but that small and sparsely occurring weeds could not be reliably detected. The results prove the limitations of remote sensing in the context of weed control but they also show that there is a great potential for early decision making for particular weed species.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 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".