Identifying yellow hawkweed in wild blueberry fields using drone images for site-specific herbicide application
Bibliographic record
Abstract
Abstract. Yellow hawkweed (Hieracium caespitosum Dumort) is a perennial weed commonly found in wild blueberry (Vaccinium angustifolium Ait.) fields in Atlantic Canada. When not managed properly, yellow hawkweed competes for space and nutrients, affecting crop yields. Current management techniques rely on broadcast herbicide applications, which are costly, contribute to the development of herbicide resistance and have negative environmental impacts. This research proposes a method to identify yellow hawkweed in drone imagery to create prescription maps for site-specific herbicide applications to improve the management of yellow hawkweed in the wild blueberry industry. A drone equipped with a DJI Zenmuse P1 optical (red, green, blue) camera was deployed to capture images to identify and segment the areas of the fields containing yellow hawkweed. Multiple models were trained and evaluated for their efficacy in detecting and segmenting yellow hawkweed. YOLO11n-seg proved to be most effective at yellow hawkweed identification. The model that achieved the highest results had a recall of 92.37% when including background images and 72.81% when excluding background images. This model was also able to correctly identify 97.11% of background images. These results indicate that yellow hawkweed can be effectively detected in drone imagery. The findings demonstrate the feasibility of drone imagery and computer vision for weed detection in wild blueberry fields.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".