Effect of Plant Characteristics on Picking Efficiency of the Wild Blueberry Harvester
Bibliographic record
Abstract
Abstract. Wild blueberry is a high value cash crop in northeastern North America. In the last two decades, improved management practices have changed crop characteristics. Currently, the wild blueberry industry is facing increased harvesting losses (15%-25%) due to changes in crop conditions. This study was designed to examine the effect of plant characteristics on picking efficiency of the wild blueberry harvester. Four wild blueberry fields were selected in Nova Scotia and New Brunswick, Atlantic Provinces of Canada. Plant height (PH) and plant density (PD) were classified into four different categories, i.e., tall plant - low plant density, tall plant - high plant density, short plant - low plant density, and short plant - high plant density, and stem thickness (ST) was used as a covariate. Nine yield plots (0.9 x 3 m) for each combination of PH and PD were selected randomly at each experimental field. The PH, PD, and ST were recorded manually from each selected plot. Factorial experiments with four replications were designed to identify the combined effect of ground speed (1.2, 1.6, and 2.0 km h-1) and header revolutions (26, 28, and 30 rpm) on berry losses at each category of PH and PD. Berry losses were collected from each plot within the selected fields. Factorial analysis of covariance (ANCOVA) using general linear model (GLM) procedure showed that the interaction of ground speed and header rpm was significant (p = 0.05) in each category of plant characteristics. Results of multiple means comparison showed that the lower ground speed and header rpm resulted in significantly lower losses when compared with higher ground speed and header rpm. The study findings also suggested a suitable combination of ground speed and header rpm for each class of plant characteristics to minimize the berry losses during harvesting.
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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 | 0.000 | 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".