Emerging Trends In Plant Disease Management: A Review Of Sustainable And Innovative Approaches
Bibliographic record
Abstract
The broad application of chemical pesticides, such as fungicides, insecticides, and bactericides that are toxic to plant pathogens or plant disease vectors, is the mainstay of plant disease control. However, the adverse effects of these chemicals and the byproducts of their decomposition could endanger both humans and the environment, which is what motivated researchers and producers to look for alternative, environmentally benign methods of disease control. Biological control agents that promote plant development, such as rhizobacteria (PGPR), are being employed more frequently in the field as alternative strategies have shown to be effective thus far. Through a variety of methods, such as the production of volatile compounds, induced systemic resistance (ISR), and antimicrobial metabolites, PGPR both directly and indirectly promotes plant growth and inhibits the development of disease in plant systems. Significant structural and functional alterations brought about by these defence mechanisms can provide disease resistance in plants. The biocontrol mechanism and proteomic viewpoint of PGPR elicitors in the management of plant diseases are discussed in the current review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".