Blueberry Producers’ Attitudes toward Harvest Mechanization for Fresh Market
Bibliographic record
Abstract
The availability and cost of agricultural labor is constraining the specialty crop industry throughout the United States. Most soft fruits destined for the fresh market are fragile and are usually hand harvested to maintain optimal quality and postharvest longevity. However, because of labor shortages, machine harvest options are being explored out of necessity. A survey on machine harvest of blueberries ( Vaccinium sp.) for fresh market was conducted in 2015 and 2016 in seven U.S. states and one Canadian province. Survey respondents totaled 223 blueberry producers of various production sizes and scope. A majority (61%) indicated that their berries were destined for fresh markets with 33% machine harvested for this purpose. Eighty percent said that they thought fruit quality was the limiting factor for machine-harvested blueberries destined for fresh markets. Many producers had used mechanized harvesters, but their experience varied greatly. Just less than half (47%) used mechanical harvesters for fewer than 5 years. Most respondents indicated that labor was a primary concern, as well as competing markets and weather. New technologies that reduce harvesting constraints, such as improvements to harvest machinery and packing lines, were of interest to most respondents. Forty-five percent stated they would be interested in using a modified harvest-aid platform with handheld shaking devices if it is viable (i.e., fruit quality and picking efficiency is maintained and the practice is cost effective). Overall, the survey showed that blueberry producers have great concerns with labor costs and availability and are open to exploring mechanization as a way to mitigate the need for hand-harvest labor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".