Quantification of Fungal Infection of Leaves with Digital Images and Scion Image Software
Bibliographic record
Abstract
Digital image analysis has been used to distinguish and quantify leaf color changes arising from a variety of factors. Its use to assess the percentage of leaf area with color differences caused by plant disease symptoms, such as necrosis, chlorosis, or sporulation, can provide a rigorous and quantitative means of assessing disease severity. A method is described for measuring symptoms of different fungal foliar infections that involves capturing the image with a standard flatbed scanner or digital camera followed by quantifying the area, where the color has been affected because of fungal infection. The method uses the freely available program, Scion Image for Windows or MAC, which is derived from the public domain software, NIH Image. The method has thus far been used to quantify the percentage of tissue with necrosis, chlorosis, or sporulation on leaves of variety of plants with several different diseases (anthracnose, apple scab, powdery mildew or rust).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".