Time-of-flight-based advanced surface reconstruction methods for real-time volume estimation of bulk harvested wild blueberries
Bibliographic record
Abstract
This study focuses on volume estimation of mechanically harvested wild blueberries contained within a harvester tote, an economically significant agricultural product for northeastern North America. Accurate volume estimation of mechanically harvested berries in their respective totes is essential for improving the efficiency of the harvesting operation. This study evaluated four computational methods for volume estimation including convex hull, alpha shape, octree, and voxel grid. The evaluation of each method was based on three key metrics: Mean absolute error (MAE), Mean absolute error bias corrected (MAEbc), and processing time. Convex hull exhibited the highest initial MAE (0.0495 ± 0.0235 m³) but showed significant error reduction post-bias correction (to 0.0122 ± 0.0128 m³), leading to a reduced variability. Alpha shape resulted in a moderate MAE (0.0379 ± 0.0217 m³) and bias-corrected error (0.0190 ± 0.0134 m 3 ), but its utility was diminished by an extended processing time (49.265 ± 2.841 s). Octree had a lower initial MAE (0.0447 ± 0.0203 m³), with large variability in bias correction effectiveness (0.0394 ± 0.0233 m³) and a moderate processing time (0.266 ± 0.015 s). Voxel grid had superior accuracy and efficiency, presenting the lowest MAE (0.0311 ± 0.0349 m³), MAEbc (0.0119 ± 0.0158 m³), as well as processing time (0.012 ± 0.001 s). These findings emphasize the importance of considering average performance and variability in method selection, particularly in fields where consistent performance is as crucial as efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".