Supplementary Light Source Development for Camera-Based Smart Spraying in Low Light Conditions
Bibliographic record
Abstract
Abstract. High wind constraints during day time agrochemical spraying has pushed the wild blueberry producers to apply agrochemicals during the early morning, evening or after dark, to avoid drift problems due to low wind conditions. The objective of this study was to develop an artificial light source system combined with a smart sprayer comprising of a digital camera-based sensing system to allow cameras to detect target areas (weed, plant or bare soil) in real-time for accurate application of agrochemicals in low light conditions. After testing and evaluation of different light sources, a rugged light source system equipped with polystyrene diffuser sheets was constructed to provide an even distribution of light across the entire 12.2 m machine vision sensor boom. Distribution of artificial light underneath the sensing boom at zero ambient light was examined by recording the light intensity at 0.15 m spacing on the ground under the camera boom using a lux meter. Results of light distribution revealed that the Magnafire® 70 W high intensity discharge (HID) lights provided wide angle of even light illumination, high intensity and rugged construction. A wild blueberry field was selected in central Nova Scotia, Canada, and a test track was made to evaluate the performance of the artificial light source system to apply agrochemicals on a spot-specific basis under low natural light conditions. A real-time kinematics-global positioning system (RTK-GPS) was used to map the boundary of the test track, selected bare soil areas, weed areas and wild blueberry plant areas in the field. Water sensitive papers (WSPs) were placed at randomly selected locations, the smart sprayer was operated under low light conditions, and the percent area coverage (PAC) was calculated. The mean PAC from WSP located in bare soil, weeds and blueberry spots in the track was 5.19%, 27.53%, and 1.74%, respectively. PAC of the WSPs placed in bare soil and blueberry patches were 22.34% and 25.79% lower than in weed patches, respectively. Results reported that the custom developed artificial light source system was accurate enough to detect targets in low light conditions. Additionally, spot-spacing only in weed areas resulted in 65% of chemical saving.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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".