Use of unmanned aerial system to assess wildlife (<i>Sus scrofa</i>) damage to crops (<i>Zea mays</i>)
Bibliographic record
Abstract
Damage caused by ungulates to agricultural areas is difficult to evaluate because the real extent of the damage remains usually poorly described and potentially leads to conflicts. Recent advances in unmanned aerial systems (UAS) provide new versatile mapping and quantification possibilities in a wide range of applications. We used crop fields (Zea mays) damaged by wild boar (Sus scrofa) and compared the extent of the damage by means of three methods: (i) traditional ground-based assessment; (ii) UAS orthoimages with operator delineation; and (iii) UAS crop height model with automatic delineation based on height threshold. We showed for the first time that UAS can be applied for assessing damage of ungulates to agriculture. The two methods using UAS imagery provide coherent and satisfactory results and tend to underestimate the damage area when compared to in-use ground-based field expertise. However, we suggest that performance of UAS should further be tested in variable conditions to assess the broad application of this tool. Our study describes the potential of UAS as a tool for estimating more accurately the damage area and subsequently the compensation costs for wildlife damage. The proposed approach can be used in support of local and regional policies for the definitions of compensation for farmers.
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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) | 0.000 | 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".