Automated, low-cost yield mapping of wild blueberry fruit
Bibliographic record
Abstract
The presence of weeds, bare spots, variation in soil/crop characteristics and fruit yield within the wild blueberry field requires more accurate applications to maximize profit and reduce environmental risks. The objective of this research was to develop an Automated Yield Monitoring and Mapping System to produce real-time fruit yield map for site-specific application of agrochemicals. This AYMMS was mounted on a specially designed Farm Motorized Vehicle (FMV). The AYMMS consists of a 10-mega pixel, 24-bit digital color camera (Canon Canada Inc., Mississauga, ON, Canada), Trimble Ag GPS 332 (Trimble Navigation Limited, Sunnyvale, CA) for geo-referencing and a laptop computer. Custom software was developed in ‘Delphi’ and ‘C’ programs for image processing to estimate %BPI (blue pixel index) representing ripe fruit. Two wild blueberry fields were selected in central Nova Scotia to evaluate the yield monitoring system. Percentage blue pixels correlated highly significantly with actual fruit yield in field 1 (R2=0.90; P<0.001; n=19) and field 2 (R2=0.97; P<0.001; n=19). The correlation between actual and predicted fruit yield in field 2 (validation) was also highly significant (R2=0.97; P<0.001; n=19). Real time yield mapping was carried out with AYMMS by acquiring images on the moving FMV at a spacing of 1.25 m and a ground speed of 0.5 m/sec. The estimated yield per image field of view along with geo-referenced coordinates was imported into ArcView 3.2 GIS (ESRI, Redlands, Calif., USA) to map fruit yield for the two selected fields. The maps showed substantial variability in fruit yield in both fields. The yield maps along with soil maps could be used for variable rate application of site specific crop inputs.
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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 | 0.001 | 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".