Deep learning for horticultural innovation: YOLOv12s revolutionizes micropropagated lingonberry phenotyping through unified phenomic-genomic-epigenomic detection
Bibliographic record
Abstract
Vaccinium vitis-idaea L. (lingonberry), globally recognized as a superfruit for its medicinal properties, has long been cultivated and consumed by Canadian Indigenous communities. This study introduces an AI-powered surveillance system that leverages an optimized You Only Look Once (YOLOv12) architecture to revolutionize yield estimation, phenomic profiling, and genomic/epigenomic analysis in micropropagated lingonberry. A custom multi-class annotated dataset was developed to evaluate model performance under real-world conditions. The YOLOv12 model, built on a RELAN backbone with flash-attention mechanisms, excelled in global context modeling, enabling accurate detection of berries and regenerated shoots in both ex vitro and in vitro environments. In contrast, YOLOv8 and YOLOv9, which rely on CNN-based feature extraction, demonstrated computational efficiency but suffered from overfitting and reduced operational robustness. In multi-class detection scenarios, YOLOv12 achieved the highest mean Average Precision with 67.3% mAP@50 in yield detection, 1.0–99.5% mAP@50 in micropropagated plant trait detection (shoots, berries, flowers), and 32.2–74% accuracy in gel electrophoresis band detection. These results reflect a 22% increase in throughput and a 38% reduction in error rates compared to conventionally human-monitored methods, significantly reducing labor cost for plant breeders and agricultural biotechnologist. The integrated system enables simultaneous monitoring of phenotypic traits across growth stages and precise molecular band analysis, establishing a new paradigm for precision agriculture and lingonberry improvement. This work establishes YOLOv12 as the first unified framework for micropropagated lingonberry phenotyping across biological scales, demonstrating labor reduction in breeding programs while maintaining operational reliability. The technology's mobile compatibility and cloud-integration potential offer immediate applications for the global $2.3B lingonberry market, particularly in precision nurseries and nutraceutical production.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".