Identification of Weed Species in Commercial Soybean Areas by High-Resolution Drone Images
Bibliographic record
Abstract
Multispectral sensors onboard remotely piloted aircraft systems (RPAs) can be used for mapping and identifying weed species and preventing crop yield losses. The objective of this study was to identify and quantify weed species in soybean using high-resolution images obtained by an RPA. Soybean fields were photographed 33 times every 100 ha. Weed flora in 384 sampling areas was surveyed by aerial imaging in approximately 60.000 ha. Results on analysis of the community structure of the observed a total of 16 plant families and 52 species. Species from Asteraceae and Poaceae were the most numerous. Results of principal component analysis showed that the percentage of infestation and the number of species were positively correlated to the first component. The areas with the highest percentage of infestation had the highest diversity of species. However, the percentage of infestation and the number of species observed were not correlated with the area size. The survey of weeds by aerial imagery was efficient for identifying, quantifying, and mapping weeds in commercial agricultural areas and can be used in other studies and for the purposes of management in commercial areas.
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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 | 0.001 | 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".