Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle for wheat aphids control
Bibliographic record
Abstract
Small unmanned aerial vehicles (UAVs) have been widely used in different aspects of modern farming management, including pest and disease control in China in recent years. In this study, the spray performance of a small plant protection UAV at low volume spray was evaluated by adjusting the pesticide dosage and adding aerial spraying adjuvants. Droplet deposition, droplet density, coverage, control effect and pesticide residue from field trials were assessed. In addition, the residue and control effect of UAV spray were compared to manual knapsack at high volume spray. The results showed that, the adjuvant applying improved the efficiency of UAV spray. Also, the adjuvant applying reduced the dosage of imidacloprid by 20%. However, there was no significant difference on initial residue between UAV spray and knapsack spray. Thus, plant protection UAV spraying pesticide by adding appropriate adjuvant showed the ability of improving the pesticide effectiveness by improving the control efficiency, reducing the pesticide dosage and residue. Keywords: unmanned aerial vehicle (UAV), aerial spraying adjuvant, deposition, control efficiency, pesticide residue, wheat aphid DOI: 10.25165/j.ijabe.20181105.4298 Citation: Meng Y H, Lan Y B, Mei G Y, Guo Y W, Song J L, Wang Z G. Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle on wheat aphids control. Int J Agric & Biol Eng, 2018; 11(5): 46–53.
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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".