Smart Sprayer for Spot-Application of Agrochemicals in Wild Blueberry Fields
Bibliographic record
Abstract
Abstract. Wild blueberry producers apply uniform blanket applications of agrochemicals without considering the significant variability in bare soil and weed coverage. The development of a smart sprayer for the wild blueberry industry is essential to minimize input costs, improve crop yield while reducing environmental pollution. The developed smart sprayer system was installed on a 12.2 m wide boom three point hitch mounted sprayer attached to a farm tractor. The modified sprayer featured an 1135 L storage capacity and 16 spray sections. Solenoid valves were connected directly to the sprayer nozzle bodies for rapid response and low drip lag. Each nozzle covered a 0.76 m wide section of the sprayer boom. The machine vision system incorporated eight digital color cameras installed ahead of the sprayer nozzles (each camera covering two spray nozzle sections) on the boom. The cameras were connected via USB cables to a ruggedized computer where custom image processing software analyzed each image. Triggering signals were sent in real-time to the individual solenoids to open the specific nozzle where the target was detected. Wild blueberry fields were selected in central Nova Scotia to evaluate the smart sprayer system. Water sensitive papers were used to quantify targeting performance at select points in the field. The smart sprayer was setup to apply a spot-application of herbicide to weed targets within the field and fungicide application to only wild blueberry plant areas within the field. The sprayer had the ability to save substantial amounts of herbicides and fungicides in commercial fields with variable weed pressures and bare spot areas.
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 | 0.000 | 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) | 0.003 | 0.001 |
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".