Combining image processing and machine learning to identify invasive plants in high-resolution images
Bibliographic record
Abstract
This study investigates the combination of image processing and supervised classification to identify invasive yellow flag iris (YFI; Iris pseudacorus) plants in images collected by an un-calibrated, visible-light camera carried aloft by an unmanned aerial vehicle. Specifically, the image-processing steps of colour thresholding, template matching, and/or de-speckling prior to training a supervised random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within an image. The impacts of performing feature selection prior to training the random forest classifier are also explored. This analysis demonstrates the importance of image processing when preparing images for classification and reveals that applying the image-processing steps of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI plants using spectral and textural features provided the best results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".