Leveraging Zero-Shot Detection Mechanisms to Accelerate Image Annotation for Machine Learning in Wild Blueberry (Vaccinium angustifolium Ait.)
Bibliographic record
Abstract
This study conducted an analysis of zero-shot detection capabilities using two frameworks, YOLO-World and Grounding DINO, on a selection of images in the wild blueberry (Vaccinium angustifolium Ait.) cropping system. The datasets included ripe wild blueberries, hair fescue (Festuca filiformis Pourr.), blueberry buds, and red leaf disease (Exobasidium vaccinii). Key performance metrics such as Intersection over Union (IoU), precision, recall, and F1 score were utilized for model comparison. Grounding DINO consistently achieved superior performance across all metrics and datasets, achieving significantly higher mean IoUs on berries, red leaf, hair fescue, and buds (0.642, 0.921, 0.735, and 0.629, respectively) compared to YOLO-World (0.516, 0.567, 0.232, and 0.408, respectively). Evidenced by their high recall rates relative to precision, the models displayed a preference for identifying true positives at the cost of increasing false positives. Grounding DINO’s higher precision (overall mean of 0.672), despite the tendency to over-detect, indicated a better balance in minimizing false positives than YOLO-World (overall mean of 0.501). These findings contrast with the foundational study of YOLO-World where it demonstrated superior performance on standard datasets, highlighting the importance of dataset characteristics and optimization processes in model performance. The practical implications of this study include providing a solution for accelerated object detection image annotation in the wild blueberry cropping system. This work, representing a significant advancement in facilitating accurate and efficient annotation of wild blueberry datasets, guides future research in the application of zero-shot detection models to agricultural datasets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".