Evaluation of a ground surface detection system using microwave radar to support automation of the wild blueberry (Vaccinium angustifolium Ait.) harvester header
Bibliographic record
Abstract
To advance automation in wild blueberry harvesting, this study evaluates the performance of three microwave radar systems—Walabot Developer, Acconeer XM112, and Terrahawk® HT5230, for ground surface detection. The ability to accurately measure ground distance while penetrating different mediums (air, grass clippings, and alfalfa hay) was assessed using controlled experiments. Results showed that the Acconeer radar exhibited the highest accuracy in air (mean absolute error: 3.0 to 12.3 mm) but performed poorly when penetrating vegetation (35.5 to 62.9 mm error). The Walabot radar demonstrated inconsistent results across all conditions. The Terrahawk® radar provided the most stable performance (mean absolute error: 46.2 to 53.9 mm), with minimal bias (-5.39 to -4.62 mm) and low standard deviation (2.5 to 8.9 mm), making it the most suitable option for further implementation. Field validation demonstrated a strong correlation (R² > 0.97) between radar-measured and ground-truthed values, confirming the potential of the Terrahawk® system for real-time ground sensing in automated harvester applications. These findings confirm the Terrahawk® radar as a viable solution for ground surface detection in the automation of the wild blueberry harvester header.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".