Spray Chamber Evaluation of Air‐Assisted Spraying on Broccoli
Bibliographic record
Abstract
Conventional over‐the‐row sprayers achieve very little deposit on leaves near the ground and on the underside surfaces of leaves throughout the canopy. Air assistance has the potential to improve deposition of droplets on these leaf surfaces. To gain some insight on the effect of air assistance, the effects of airspeed, airflow rate, and air jet orientation were isolated. The study was carried out in a spray chamber with a standard spray boom over micro‐plots of greenhouse grown broccoli (Brassica oleracea var. botrytis L.) plants. Air was delivered slightly behind the nozzles from a variable width slot producing a uniform two‐dimensional air jet. The orientation of the air jet with respect to the vertical could be adjusted from −10 to 40°. The ranges of the independent variables were airspeed, 0 to 36 m s−1; airflow rate, 0 to 1.3 m3 s−1 m−1, and air jet angle, −10.2 to 40.2°. Two sets of flat fan nozzles (Volume Median Diameter = 230 and 400 μm, both delivering 250 L ha−1 at 6 km h−1) were used to carry out two full sets of experiments. Results showed that airspeed had the larger impact on leaf coverage. Higher airspeeds (>25 m s−1) and airflow coupled with finer sprays increased the coverage of the underside of the leaves at all levels within the canopy and of the top side of the leaves in the lower third of the canopy. However, lower airspeeds (<20 m s−1) are desirable for a better coverage of the upper side of the leaves in the higher two‐thirds of the canopy. In all cases, angling the air jet forward at 20 to 25° is recommended.
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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 | 0.000 | 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) | 0.001 | 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".