Application of molecular methods for potato disease diagnosis: a review
Bibliographic record
Abstract
Potato is a vital crop worldwide and its production is threatened by diseases caused by viruses, bacteria, fungi and other forms of microorganisms. Early detection and identification of these pathogens are essential to control their spread and minimize yield losses. Improvements in detection and identification have come as molecular detection techniques have been developed, such as Enzyme-Linked Immunosorbent Assay (ELISA), Polymerase Chain Reaction (PCR), Quantitative real-time PCR (qPCR), RNase H-dependent PCR (rhPCR) and Loop-Mediated Isothermal Amplification (LAMP). In this review, the mechanisms of these molecular techniques are discussed. These techniques have revolutionized the diagnosis and management of potato diseases. They offer high sensitivity, specificity and rapid detection, making them invaluable tools for disease surveillance and diagnosis. However, further development and optimization of these techniques, and novel techniques such as artificial intelligence, will undoubtedly contribute to more effective disease diagnosis, management and the protection of potato crops.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".