Could Weed Sensing in Corn Interrows Result in Efficient Weed Control?
Bibliographic record
Abstract
At the field scale, weeds generally appear aggregated rather than randomly distributed, and this aggregation is linked to the spatial heterogeneity of biotic and abiotic factors. Crop management practices shape the spatial pattern of weed infestations by modifying certain factors having an impact on weed emergence and growth. Although crop seeding is often the last in-field disturbance before crop and weed emergence, its effect on the distribution of weeds has received little attention in the literature. The purpose of this study was to assess the influence of the planting operation on weed cover and presence in corn fields using digital images to investigate the possibility of sensing the interrow to infer the presence or absence of weeds on the corn row. A total of 18 site-years under conventional tillage treated with a single POST application of herbicide were selected across seven locations. Image analysis, at the V2 to V4 growth stage of corn, was used to compare the weed cover in three zones: the undisturbed interrows, the corn rows, and the interrows compacted by tractor wheel traffic. For 61% of site-years, there was no significant difference among the zones. When there was a significant difference compared with the other two zones, the undisturbed interrow was usually less infested. Point-to-point comparisons of weed presence or absence (based on a threshold of five pixels) between the interrow and the corn row revealed 70 or 73% correspondence, depending on the type of interrow (undisturbed or tracked). However the error of inference of the corn row weed cover generated by sensing only adjacent interrows may be too high for efficient commercial weed control.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".