Biocontrol of Rice Seed-Associated Fungal Pathogens Using Green Synthesis Approaches
Bibliographic record
Abstract
Rice ( Oryza sativa ) is a major cereal crop that balances the food demand of the worldwide population. The crop quality drops daily due to their exposure to biotic and abiotic stresses, especially pathogens. It needs to be improved to maintain the consumption level to cope with increasing population demands for food. The current study was designed to analyze the comparison of the effects of green synthesis approaches on pathogens associated with rice seeds. In this study, essential oils were extracted from Cymbopogon citratus , Thymus vulgaris , and Origanum vulgaris medicinal plants and used as fungicides on fungal strains of Aspergillus spp. T. vulgaris effectively controlled the growth of Aspergillus niger , Aspergillus flavus , and Aspergillus terreus as compared with O. vulgaris and Cymbopogon . Further, silica nanoparticles (SiNPs) were synthesized from rice husk to evaluate their antifungal activities. SiNPs were characterized by ultraviolet-visible spectroscopy with a broad peak at 281.62 nm. Fourier-transform infrared spectroscopy spectrum confirms the presence of Si–H, Si–OH, and Si–O–Si bonds functional groups, and SiO 4 tetrahedral coordination unit. X-ray diffraction pattern describes the crystalline structure with a sharp peak at 2θ = 22°. Scanning electron microscopy and energy-dispersive spectroscopy confirmed the spherical shape, size 70–115 nm, and elemental composition with pure silica contents. SiNPs showed no significant antifungal activity against Aspergillus strains. Moreover, Trichoderma was isolated from the rhizosphere of rice fields and showed a surprising antifungal effect against A. terreus , A. niger , and A. flavus. The current study successfully revealed environment-friendly and cost-effective green synthesizing approaches for analyzing biocontrol potential against rice seed-related Aspergillus spp. They will also help to improve pathogen control strategies in other cereals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".