Utilizing image processing techniques to compute herbivory.
Bibliographic record
Abstract
Leafy spurge (Euphorbia esula L. sensu lato) is a perennial weed species common to the north-central United States and southern Canada. The plant is a foreign species toxic to cattle. Spurge infestation can reduce cattle carrying capacity by 50 to 75 percent [1]. University of Wyoming Entomology doctoral candidate Vonny Barlow is conducting research in the area of biological control of leafy spurge via the Aphthona nigriscutis Foudras flea beetle. He is addressing the question of variability within leafy spurge and its potential impact on flea beetle herbivory. One component of Barlow's research consists of measuring the herbivory of leafy spurge plant specimens after introducing adult beetles. Herbivory is the degree of consumption of the plant's leaves and was measured in two different manners. First, Barlow assigned each consumed plant specimen a visual rank from 1 to 5. Second, image processing techniques were applied to "before" and "after" images of each plant specimen in an attempt to more accurately quantify herbivory. Standardized techniques were used to acquire images before and after beetles were allowed to feed on plants for a period of 12 days. Matlab was used as the image processing tool. The image processing algorithm allowed the user to crop the portion of the "before" image containing only plant foliage. Then Matlab cropped the "after" image with the same dimensions, converted the images from RGB to grayscale. The grayscale image was converted to binary based on a user defined threshold value. Finally, herbivory was computed based on the number of black pixels in the "before" and "after" images. The image processing results were mixed. Although, this image processing technique depends on user input and non-ideal images, the data is useful to Barlow's research and offers insight into better imaging systems and processing algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".