A PROCEDURE FOR IDENTIFYING INVASIVE WILD PARSNIP PLANTS BASED ON VISIBLE BANDS FROM UAV IMAGES
Bibliographic record
Abstract
Abstract. Wild parsnip is an invasive plant that has serious health risks to humans due to the toxin in its sap. Monitoring its presence has been a challenging task for conservation authorities due to its small size and irregular shape. Unmanned Aerial Vehicles (UAV) can obtain ultra-high resolution (UHR) imagery and have been used for vegetation monitoring in recent years. In this study, UAV images captured at Lemoine Point Conservation Area in Kingston, Ontario, are used to test a methodology for distinguishing wild parsnip. The objective of this study is to develop an efficient invasive wild parsnip classification workflow based on UHR digital UAV imagery. Image pre-processing flow includes image orientation, digital elevation model (DEM) and digital surface model (DSM) extractions, and orthomosaicking using Simactive’s software. Three vegetation indices and three texture features are calculated and added to the mosaicked images as additional bands. Image analysis frameworks namely pixel- and object-based method and three classifiers are tested and the object-based Support Vector Machine (SVM) is selected to distinguish wild parsnip from other vegetation types. The optimal image resolutions are undertaken by comparing accuracy assessments. The results provide an executable workflow to distinguish wild parsnip and show that UAV images, with a simple digital camera, are an appropriate and economic resource for small and irregular vegetation detection. This method yields reliable and valid outcomes in detecting wild parsnip plants and demonstrates excellent performance in mapping small vegetation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".