Potential Use of Digital Photographic Technique to Examine Wild Blueberry Ripening in Relation to Time of Harvest
Bibliographic record
Abstract
Abstract. Northeastern North America is the world’s leading producer of wild blueberry. Ripening of wild blueberry is the leading factor for fruit quality. Currently, there are no protocols available for the farming community related to wild blueberry fruit ripening and maturity. A nondestructive, rapid, and reliable digital photography technique could be used to examine the ripening of wild blueberries for appropriate harvesting time. Two wild blueberry fields were selected to examine the berry ripening levels using digital photographic techniques at different time of harvest (early, middle, and late seasons). The fields were divided into four blocks and each block was further divided into three classes of times of harvest. Fruit images from each block were acquired and processed to count blue pixels from each image. A significant correlation was found between percentage of blue pixels and actual fruit yield in Field A (R2 = 0.96; P < 0.001) and Field B (R2 = 0.97; P < 0.001). The results also indicated that the effect of time of harvest on fruit yield was significant and fruit yield increased gradually during early harvesting, reached maximum during mid-season, and then started to decrease in late harvesting. Results indicated that 90% of green-berries had turned blue at the end of middle season compared to early season (58%). Based on the results of this study, optical analysis could help to keep fruit quality by optimizing appropriate harvesting time of wild blueberries. It is also suggested that the optimum time to harvest wild blueberries is middle season to ensure high fruit yield and quality. Keywords: Blue pixels, Fruit yield, Harvesting season, Wild blueberry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".