Image-Based Quantification of Plant Immunity and Disease
Bibliographic record
Abstract
Measuring the extent and severity of disease is a critical component of plant pathology research and crop breeding. Unfortunately, existing visual scoring systems are qualitative, subjective, and the results are difficult to transfer between research groups, while existing quantitative methods can be quite laborious. Here, we present plant immunity and disease image-based quantification (PIDIQ), a quantitative, semi-automated system to rapidly and objectively measure disease symptoms in a biologically relevant context. PIDIQ applies an ImageJ-based macro to plant photos in order to distinguish healthy tissue from tissue that has yellowed due to disease. It can process a directory of images in an automated manner and report the relative ratios of healthy to diseased leaf area, thereby providing a quantitative measure of plant health that can be statistically compared with appropriate controls. We used the Arabidopsis thaliana-Pseudomonas syringae model system to show that PIDIQ is able to identify both enhanced plant health associated with effector-triggered immunity as well as elevated disease symptoms associated with effector-triggered susceptibility. Finally, we show that the quantitative results provided by PIDIQ correspond to those obtained via traditional in planta pathogen growth assays. PIDIQ provides a simple and effective means to nondestructively quantify disease from whole plants and we believe it will be equally effective for monitoring disease on excised leaves and stems.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".