Advanced diagnostic approaches developed for the global menace of rice diseases: a review
Bibliographic record
Abstract
For centuries, rice has been the one of the most important crops worldwide; over 3.5 billion of the population relies on rice as one of the most essential staple foods. However, rice diseases are responsible for enormous global economic losses due to the damage they cause to rice crops. Major rice diseases such as bacterial leaf blight (BLB), rice blast, sheath blight (SB), bacterial panicle blight (BPB), and the rice tungro disease can contribute to a great reduction in rice yield annually. Therefore, the availability of sensitive and accurate diagnostic tools in plant disease detection is necessary to facilitate effective management practices. Traditional methods are not the best to deploy in plant disease detection, as they can be time-consuming and unreliable. Hence, the development of serological, molecular, image processing, and biosensor techniques provide important tools for accurate disease diagnosis and precise identification of plant pathogens. This review summarizes BLB, rice blast, SB, BPB, and rice tungro disease, and discusses valuable diagnostic approaches by outlining the advantages and disadvantages of each tool based on previous studies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".