Evaluation of Remotely Piloted Aircraft for Agricultural Spraying in Corn Cultivation in the Brazilian Savannah
Bibliographic record
Abstract
This study aimed to evaluate the quality of application using both RPA and ground techniques in corn cultivation using different spray nozzles in Brazilian savannah (Cerrado). The experiment was conducted using a completely randomized design, with four treatments and eight repetitions. The treatments involved two methods of application: aerial (RPA, 10 L ha-1) and ground (backpack sprayer, 100 L ha-1); and two types of nozzles: the standard flat fan nozzle XR 11001 and the air induction flat fan nozzle AirMix 11001. To study the quality of the application, the deposition was evaluated using a tracer and spectrophotometry, and the droplet spectrum, evaluated through the analysis of water-sensitive paper. Furthermore, a study on application quality was conducted using the Statistical Process Control methodology. The results demonstrated that with RPA, the deposition was higher, and the AirMix 11001 nozzle also stood out in this variable. No non-random behavior was observed in the deposition. Ground application showed the best performance in terms of target coverage and droplet density. The XR 11001 nozzle resulted in a higher droplet density. Overall, the XR11001 nozzle and the RPA application showed smaller volume median diameters (VMDs), indicating a higher potential for drift, which should be taken into account. Given the results obtained, it can be stated that the use of RPAs for agricultural spraying is viable for application.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".